Week 2 Assignment – Case Study: Introduction to Software Architecture
Introduction
Read the following case studies in the article,
Introduction to Software Architecture [PDF]
, and complete this case study assignment.
· 4.1 Case Study 1: Key Word in Context.
· 4.2 Case Study 2: Instrumentation Software.
· 4.3 Case 3: A fresh View of Compilers.
The article is an overview of the topic of software architecture and contains several brief case studies in Chapter 4. Each case study offers its own attributes in regard to the approach of software architecture that the author is addressing and when read together it allows for compressions to be made.
Instructions
Write a 5–6 page paper that:
1. Identify both the similarities and the challenges that the architects faced in each of the cases.
2. Compare and contrast each architectural solution and determine which solution provided the optimal results in the first case study, Key World in Context. Justify your answer.
3. Compare and contrast the differences in architecting systems that are specific to a particular domain versus systems that are far less domain-specific.
4. Explain how developing domain-specific software affects the planning and testing phases of the software development life cycle.
5. Given the flow of process involved with compiling code, select another application that is not related to compiling or intercepting code that could borrow from either architecture of compilers presented in the third case. Justify your selection.
6. Explain how architecting systems provide a means to deliver a product that was in line with the requirements based on the information you gathered from the three cases.
This course requires the use of Strayer Writing Standards. For assistance and information, please refer to the Strayer Writing Standards link in the left-hand menu of your course. Check with your professor for any additional instructions.
The specific course learning outcome associated with this assignment is:
· Examine the different methods and processes a software program architect uses to combat challenges they face.
An Introduction to Software Architectur
e
David Garlan and Mary Shaw
January 1994
CMU-CS-94-166
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213-3890
Also published as “An Introduction to Software Architecture,” Advances in Software Engineering
and Knowledge Engineering, Volume I, edited by V.Ambriola and G.Tortora, World Scientific
Publishing Company, New Jersey, 1993.
Also appears as CMU Software Engineering Institute Technical Report
CMU/SEI-94-TR-21, ESC-TR-94-21.
©1994 by David Garlan and Mary Shaw
This work was funded in part by the Department of Defense Advanced Research Project Agency under grant
MDA972-92-J-1002, by National Science Foundation Grants CCR-9109469 and CCR-9112880, and by a grant
from Siemens Corporate Research. It was also funded in part by the Carnegie Mellon University School of
Computer Science and Software Engineering Institute (which is sponsored by the U.S. Department of Defense).
The views and conclusions contained in this document are those of the authors and should not be interpreted
as representing the official policies, either expressed or implied, of the U.S. Government, the Department of
Defense, the National Science Foundation, Siemens Corporation, or Carnegie Mellon University.
Keywords: Software architecture, software design, software engineering
Abstract
As the size of software systems increases, the algorithms and data structures of
the computation no longer constitute the major design problems. When
systems are constructed from many components, the organization of the
overall system—the software architecture—presents a new set of design
problems. This level of design has been addressed in a number of way
s
including informal diagrams and descriptive terms, module interconnection
languages, templates and frameworks for systems that serve the needs of
specific domains, and formal models of component integration mechanisms.
In this paper we provide an introduction to the emerging field of software
architecture. We begin by considering a number of common architectural
styles upon which many systems are currently based and show how different
styles can be combined in a single design. Then we present six case studies to
illustrate how architectural representations can improve our understanding of
complex software systems. Finally, we survey some of the outstanding
problems in the field, and consider a few of the promising research directions.
Contents
1. Introduction ……………………………………………………………………………………….. 2
2. From Programming Languages to Software Architecture………………… 3
2.1. High-level Programming Languages …………………………………………………………. 3
2.2. Abstract Data Types………………………………………………………………………………. 4
2.3. Software Architecture……………………………………………………………………………. 4
3. Common Architectural Styles……………………………………………………………. 5
3.1. Pipes and Filters …………………………………………………………………………………… 6
3.2. Data Abstraction and Object-Oriented Organization ……………………………………. 8
3.3. Event-based, Implicit Invocation ……………………………………………………………… 9
3.4. Layered Systems …………………………………………………………………………………… 11
3.5. Repositories …………………………………………………………………………………………. 12
3.6. Table Driven Interpreters ……………………………………………………………………….. 13
3.7. Other Familiar Architectures………………………………………………………………….. 14
3.8. Heterogeneous Architectures……………………………………………………………………. 15
4. Case Studies………………………………………………………………………………………… 16
4.1. Case Study 1: Key Word in Context …………………………………………………………… 16
4.2. Case Study 2: Instrumentation Software …………………………………………………….. 22
4.3. Case 3: A Fresh View of Compilers …………………………………………………………… 26
4.4. Case 4: A Layered Design with Different Styles for the Layers ……………………… 28
4.5. Case 5: An Interpreter Using Different Idioms for the Components…………………… 30
4.6. Case 6: A Blackboard Globally Recast as Interpreter ……………………………………. 33
5. Past, Present, and Future ……………………………………………………………………. 36
Acknowledgements………………………………………………………………………………………. 37
Bibliography ………………………………………………………………………………………………….. 37
Garlan & Shaw: An Introduction to Software Architecture 1
List of Figures
1 Pipes and Filters …………………………………………………………………………………………. 7
2 Abstract Data Types and Objects…………………………………………………………………. 8
3 Layered Systems ……………………………………………………………………………………….. 11
4 The Blackboard…………………………………………………………………………………………. 13
5 Interpreter…………………………………………………………………………………………………. 14
6 KWIC – Shared Data Solution………………………………………………………………….. 18
7 KWIC – Abstract Data Type Solution……………………………………………………….. 19
8 KWIC – Implicit Invocation Solution ……………………………………………………… 20
9 KWIC – Pipe and Filter Solution ……………………………………………………………… 20
10 KWIC – Comparison of Solutions……………………………………………………………. 21
11 Oscilloscopes – An Object-oriented Model……………………………………………….. 23
12 Oscilloscopes – A Layered Model ……………………………………………………………… 24
13 Oscilloscopes – A Pipe and Filter Model…………………………………………………… 24
14 Oscilloscopes – A Modified Pipe and Filter Model…………………………………… 25
15 Traditional Compiler Model……………………………………………………………………. 26
16 Traditional Compiler Model with Shared Symbol Table ……………………….. 26
17 Modern Canonical Compiler …………………………………………………………………… 27
18 Canonical Compiler, Revisited………………………………………………………………… 27
19 PROVOX – Hierarchical Top Level…………………………………………………………… 28
20 PROVOX – Object-oriented Elaboration …………………………………………………… 29
21 Basic Rule-Based System………………………………………………………………………….. 31
22 Sophistocated Rule-Based System……………………………………………………………. 32
23 Simplified Rule-Based System…………………………………………………………………. 33
24 Hearsay-II………………………………………………………………………………………………….. 34
25 Blackboard View of Hearsay-II…………………………………………………………………. 35
26 Interpreter View of Hearsay-II …………………………………………………………………. 36
Garlan & Shaw: An Introduction to Software Architecture 2
1. Introduction
As the size and complexity of software systems increases, the design problem
goes beyond the algorithms and data structures of the computation: designing
and specifying the overall system structure emerges as a new kind of problem.
Structural issues include gross organization and global control structure;
protocols for communication, synchronization, and data access; assignment of
functionality to design elements; physical distribution; composition of design
elements; scaling and performance; and selection among design alternatives.
This is the software architecture level of design. There is a considerable
body of work on this topic, including module interconnection languages,
templates and frameworks for systems that serve the needs of specific domains,
and formal models of component integration mechanisms. In addition, an
implicit body of work exists in the form of descriptive terms used informally to
describe systems. And while there is not currently a well-defined terminology
or notation to characterize architectural structures, good software engineers
make common use of architectural principles when designing complex
software. Many of the principles represent rules of thumb or idiomatic
patterns that have emerged informally over time. Others are more carefully
documented as industry and scientific standards.
It is increasingly clear that effective software engineering requires facility in
architectural software design. First, it is important to be able to recognize
common paradigms so that high-level relationships among systems can be
understood and so that new systems can be built as variations on old systems.
Second, getting the right architecture is often crucial to the success of a software
system design; the wrong one can lead to disastrous results. Third, detailed
understanding of software architectures allows the engineer to make
principled choices among design alternatives. Fourth, an architectural system
representation is often essential to the analysis and description of the high-
level properties of a complex system.
In this paper we provide an introduction to the field of software
architecture. The purpose is to illustrate the current state of the discipline and
examine the ways in which architectural design can impact software design.
The material presented here is selected from a semester course, Architectures
for Software Systems, taught at CMU by the authors [1]. Naturally, a short
paper such as this can only briefly highlight the main features of the terrain.
This selection emphasizes informal descriptions omitting much of the course’s
material on specification, evaluation, and selection among design alternatives.
We hope, nonetheless, that this will serve to illuminate the nature and
significance of this emerging field.
In the following section we outline a number of common architectural
styles upon which many systems are currently based, and show how
Garlan & Shaw: An Introduction to Software Architecture 3
heterogeneous styles can be combined in a single design. Next we use six case
studies to illustrate how architectural representations of a software system can
improve our understanding of complex systems. Finally, we survey some of
the outstanding problems in the field, and consider a few of the promising
research directions.
The text that makes up the bulk of this article has been drawn from
numerous other publications by the authors. The taxonomy of architectural
styles and the case studies have incorporated parts of several published papers
[1, 2, 3, 4]. To a lesser extent material has been drawn from other articles by the
authors [5, 6, 7].
2. From Programming Languages to Software Architecture
One characterization of progress in programming languages and tools has been
regular increases in abstraction level—or the conceptual size of software
designers building blocks. To place the field of Software Architecture into
perspective let us begin by looking at the historical development of abstraction
techniques in computer science.
2.1. High-level Programming Languages
When digital computers emerged in the 1950s, software was written in
machine language; programmers placed instructions and data individually and
explicitly in the computer’s memory. Insertion of a new instruction in a
program might require hand-checking of the entire program to update
references to data and instructions that moved as a result of the insertion.
Eventually it was recognized that the memory layout and update of references
could be automated, and also that symbolic names could be used for operation
codes, and memory addresses. Symbolic assemblers were the result. They were
soon followed by macro processors, which allowed a single symbol to stand for
a commonly-used sequence of instructions. The substitution of simple
symbols for machine operation codes, machine addresses yet to be defined, and
sequences of instructions was perhaps the earliest form of abstraction in
software.
In the latter part of the 1950s, it became clear that certain patterns of
execution were commonly useful—indeed, they were so well understood that
it was possible to create them automatically from a notation more like
mathematics than machine language. The first of these patterns were for
evaluation of arithmetic expressions, for procedure invocation, and for loops
and conditional statements. These insights were captured in a series of early
high-level languages, of which Fortran was the main survivor.
Higher-level languages allowed more sophisticated programs to be
developed, and patterns in the use of data emerged. Whereas in Fortran data
types served primarily as cues for selecting the proper machine instructions,
Garlan & Shaw: An Introduction to Software Architecture 4
data types in Algol and it successors serve to state the programmer’s intentions
about how data should be used. The compilers for these languages could build
on experience with Fortran and tackle more sophisticated compilation
problems. Among other things, they checked adherence to these intentions,
thereby providing incentives for the programmers to use the type mechanism.
Progress in language design continued with the introduction of modules to
provide protection for related procedures and data structures, with the
separation of a module’s specification from its implementation, and with the
introduction of abstract data types.
2.2. Abstract Data Types
In the late 1960s, good programmers shared an intuition about software
development: If you get the data structures right, the effort will make
development of the rest of the program much easier. The abstract data type
work of the 1970s can be viewed as a development effort that converted this
intuition into a real theory. The conversion from an intuition to a theory
involved understanding
• the software structure (which included a representation packaged with
its primitive operators),
• specifications (mathematically expressed as abstract models or algebraic
axioms),
• language issues (modules, scope, user-defined types),
• integrity of the result (invariants of data structures and protection from
other manipulation),
• rules for combining types (declarations),
• information hiding (protection of properties not explicitly included in
specifications).
The effect of this work was to raise the design level of certain elements of
software systems, namely abstract data types, above the level of programming
language statements or individual algorithms. This form of abstraction led to
an understanding of a good organization for an entire module that serves one
particular purpose. This involved combining representations, algorithms,
specifications, and functional interfaces in uniform ways. Certain support was
required from the programming language, of course, but the abstract data type
paradigm allowed some parts of systems to be developed from a vocabulary of
data types rather than from a vocabulary of programming-language constructs.
2.3. Software Architecture
Just as good programmers recognized useful data structures in the late 1960s,
good software system designers now recognize useful system organizations.
Garlan & Shaw: An Introduction to Software Architecture 5
One of these is based on the theory of abstract data types. But this is not the
only way to organize a software system.
Many other organizations have developed informally over time, and are
now part of the vocabulary of software system designers. For example, typical
descriptions of software architectures include synopses such as (italics ours):
•“Camelot is based on the client-server model and uses remote procedure
calls both locally and remotely to provide communication among
applications and servers.”[8]
•“Abstraction layering and system decomposition provide the appearance
of system uniformity to clients, yet allow Helix to accommodate a
diversity of autonomous devices. The architecture encourages a client-
server model for the structuring of applications.”[9]
•“We have chosen a distributed, object-oriented approach to managing
information.” [10]
•“The easiest way to make the canonical sequential compiler into a
concurrent compiler is to pipeline the execution of the compiler phases
over a number of processors. . . . A more effective way [is to] split the
source code into many segments, which are concurrently processed
through the various phases of compilation [by multiple compiler
processes] before a final, merging pass recombines the object code into a
single program.”[11]
Other software architectures are carefully documented and often widely
disseminated. Examples include the International Standard Organization’s
Open Systems Interconnection Reference Model (a layered network
architecture) [12], the NIST/ECMA Reference Model (a generic software
engineering environment architecture based on layered communication
substrates) [13, 14], and the X Window System (a distributed windowed user
interface architecture based on event triggering and callbacks) [15].
We are still far from having a well-accepted taxonomy of such architectural
paradigms, let alone a fully-developed theory of software architecture. But we
can now clearly identify a number of architectural patterns, or styles, that
currently form the basic repertoire of a software architect.
3. Common Architectural Styles
We now examine some of these representative, broadly-used architectural
styles. Our purpose is to illustrate the rich space of architectural choices, and
indicate what are some of the tradeoffs in choosing one style over another.
To make sense of the differences between styles, it helps to have a common
framework from which to view them. The framework we will adopt is to treat
an architecture of a specific system as a collection of computational
Garlan & Shaw: An Introduction to Software Architecture 6
components—or simply components-—together with a description of the
interactions between these components—the connectors. Graphically speaking,
this leads to a view of an abstract architectural description as a graph in which
the nodes represent the components and the arcs represent the connectors. As
we will see, connectors can represent interactions as varied as procedure call,
event broadcast, database queries, and pipes.
An architectural style, then, defines a family of such systems in terms of a
pattern of structural organization. More specifically, an architectural style
determines the vocabulary of components and connectors that can be used in
instances of that style, together with a set of constraints on how they can be
combined. These can include topological constraints on architectural
descriptions (e.g., no cycles). Other constraints—say, having to do with
execution semantics—might also be part of the style definition.
Given this framework, we can understand what a style is by answering the
following questions: What is the structural pattern—the components,
connectors, and constraints? What is the underlying computational model?
What are the essential invariants of the style? What are some common
examples of its use? What are the advantages and disadvantages of using that
style? What are some of the common specializations?
3.1. Pipes and Filters
In a pipe and filter style each component has a set of inputs and a set of
outputs. A component reads streams of data on its inputs and produces
streams of data on its outputs, delivering a complete instance of the result in a
standard order. This is usually accomplished by applying a local
transformation to the input streams and computing incrementally so output
begins before input is consumed. Hence components are termed “filters”. The
connectors of this style serve as conduits for the streams, transmitting outputs
of one filter to inputs of another. Hence the connectors are termed “pipes”.
Among the important invariants of the style, filters must be independent
entities: in particular, they should not share state with other filters. Another
important invariant is that filters do not know the identity of their upstream
and downstream filters. Their specifications might restrict what appears on the
input pipes or make guarantees about what appears on the output pipes, but
they may not identify the components at the ends of those pipes. Furthermore,
the correctness of the output of a pipe and filter network should not depend on
the order in which the filters perform their incremental processing—although
fair scheduling can be assumed. (See [5] for an in-depth discussion of this style
and its formal properties.) Figure 1 illustrates this style.
Common specializations of this style include pipelines, which restrict the
topologies to linear sequences of filters; bounded pipes, which restrict the
amount of data that can reside on a pipe; and typed pipes, which require that
the data passed between two filters have a well-defined type.
Garlan & Shaw: An Introduction to Software Architecture 7
Data flowASCII stream
filterC
omputation
Figure 1: Pipes and Filters
A degenerate case of a pipeline architecture occurs when each filter
processes all of its input data as a single entity.1 In this case the architecture
becomes a “batch sequential” system. In these systems pipes no longer serve
the function of providing a stream of data, and therefore are largely vestigial.
Hence such systems are best treated as instances of a separate architectural style.
The best known examples of pipe and filter architectures are programs
written in the Unix shell [16]. Unix supports this style by providing a notation
for connecting components (represented as Unix processes) and by providing
run time mechanisms for implementing pipes. As another well-known
example, traditionally compilers have been viewed as a pipeline systems
(though the phases are often not incremental). The stages in the pipeline
include lexical analysis, parsing, semantic analysis, code generation. (We
return to this example in the case studies.) Other examples of pipes and filters
occur in signal processing domains [17], functional programming [18], and
distributed systems [19].
Pipe and filter systems have a number of nice properties. First, they allow
the designer to understand the overall input/output behavior of a system as a
simple composition of the behaviors of the individual filters. Second, they
support reuse: any two filters can be hooked together, provided they agree on
the data that is being transmitted between them. Third, systems can be easily
maintained and enhanced: new filters can be added to existing systems and old
filters can be replaced by improved ones. Fourth, they permit certain kinds of
specialized analysis, such as throughput and deadlock analysis. Finally, they
naturally support concurrent execution. Each filter can be implemented as a
separate task and potentially executed in parallel with other filters.
But these systems also have their disadvantages.2 First, pipe and filter
systems often lead to a batch organization of processing. Although filters can
1In general, we find that the boundaries of styles can overlap. This should not deter us from
identifying the main features of a style with its central examples of use.
2This is true in spite of the fact that pipes and filters, like every style, has a set of devout
religious followers—people who believe that all problems worth solving can best be solved using
that particular style.
Garlan & Shaw: An Introduction to Software Architecture 8
process data incrementally, since filters are inherently independent, the
designer is forced to think of each filter as providing a complete
transformation of input data to output data. In particular, because of their
transformational character, pipe and filter systems are typically not good at
handling interactive applications. This problem is most severe when
incremental display updates are required, because the output pattern for
incremental updates is radically different from the pattern for filter output.
Second, they may be hampered by having to maintain correspondences
between two separate, but related streams. Third, depending on the
implementation, they may force a lowest com-
mon denominator on data transmission, resulting in added work for each
filter to parse and unparse its data. This, in turn, can lead both to loss of
performance and to increased complexity in writing the filters themselves.
3.2. Data Abstraction and Object-Oriented Organization
In this style data representations and their associated primitive operations are
encapsulated in an abstract data type or object. The components of this style are
the objects—or, if you will, instances of the abstract data types. Objects are
examples of a sort of component we call a manager because it is responsible for
preserving the integrity of a resource (here the representation). Objects interact
through function and procedure invocations. Two important aspects of this
style are (a) that an object is responsible for preserving the integrity of its
representation (usually by maintaining some invariant over it), and (b) that
the representation is hidden from other objects. Figure 2 illustrates this style.3
obj
obj
obj
obj
obj
obj
objobj
op
op
op
op
op
op
op
op
op
op
op
op
op
op
op
op
obj is a manager
op is an invocation
ADTManager
Proc call
Figure 2: Abstract Data Types and Objects
3We haven’t mentioned inheritance in this description. While inheritance is an important
organizing principle for defining the types of objects in a system, it does not have a direct
architectural function. In particular, in our view, an inheritance relationship is not a connector,
since it does not define the interaction between components in a system. Also, in an architectural
setting inheritance of properities is not restricted to object types—but may include connectors and
even architectural styles.
Garlan & Shaw: An Introduction to Software Architecture 9
The use of abstract data types, and increasingly the use of object-oriented
systems, is, of course, widespread. There are many variations. For example,
some systems allow “objects” to be concurrent tasks; others allow objects to
have multiple interfaces [20, 21].
Object-oriented systems have many nice properties, most of which are well
known. Because an object hides its representation from its clients, it is possible
to change the implementation without affecting those clients. Additionally,
the bundling of a set of accessing routines with the data they manipulate
allows designers to decompose problems into collections of interacting agents.
But object-oriented systems also have some disadvantages. The most
significant is that in order for one object to interact with another (via
procedure call) it must know the identity of that other object. This is in
contrast, for example, to pipe and filter systems, where filters do need not
know what other filters are in the system in order to interact with them. The
significance of this is that whenever the identity of an object changes it is
necessary to modify all other objects that explicitly invoke it. In a module-
oriented language this manifests itself as the need to change the “import” list
of every module that uses the changed module. Further there can be side-
effect problems: if A uses object B and C also uses B, then C’s effects on B look
like unexpected side effects to A, and vice versa.
3.3. Event-based, Implicit Invocation
Traditionally, in a system in which the component interfaces provide a
collection of procedures and functions, components interact with each other by
explicitly invoking those routines. However, recently there has been
considerable interest in an alternative integration technique, variously referred
to as implicit invocation, reactive integration, and selective broadcast. This
style has historical roots in systems based on actors [22], constraint satisfaction,
daemons, and packet-switched networks.
The idea behind implicit invocation is that instead of invoking a
procedure directly, a component can announce (or broadcast) one or more
events. Other components in the system can register an interest in an event by
associating a procedure with the event. When the event is announced the
system itself invokes all of the procedures that have been registered for the
event. Thus an event announcement “implicitly” causes the invocation of
procedures in other modules.
For example, in the Field system [23], tools such as editors and variable
monitors register for a debugger’s breakpoint events. When a debugger stops at
a breakpoint, it announces an event that allows the system to automatically
invoke methods in those registered tools. These methods might scroll an
editor to the appropriate source line or redisplay the value of monitored
variables. In this scheme, the debugger simply announces an event, but does
Garlan & Shaw: An Introduction to Software Architecture 10
not know what other tools (if any) are concerned with that event, or what they
will do when that event is announced.
Architecturally speaking, the components in an implicit invocation style
are modules whose interfaces provide both a collection of procedures (as with
abstract data types) and a set of events. Procedures may be called in the usual
way. But in addition, a component can register some of its procedures with
events of the system. This will cause these procedures to be invoked when
those events are announced at run time. Thus the connectors in an implicit
invocation system include traditional procedure call as well as bindings
between event announcements and procedure calls.
The main invariant of this style is that announcers of events do not know
which components will be affected by those events. Thus components cannot
make assumptions about order of processing, or even about what processing,
will occur as a result of their events. For this reason, most implicit invocation
systems also include explicit invocation (i.e., normal procedure call) as a
complementary form of interaction.
Examples of systems with implicit invocation mechanisms abound [7].
They are used in programming environments to integrate tools [23, 24], in
database management systems to ensure consistency constraints [22, 25], in user
interfaces to separate presentation of data from applications that manage the
data [26, 27], and by syntax-directed editors to support incremental semantic
checking [28, 29].
One important benefit of implicit invocation is that it provides strong
support for reuse. Any component can be introduced into a system simply by
registering it for the events of that system. A second benefit is that implicit
invocation eases system evolution [30]. Components may be replaced by other
components without affecting the interfaces of other components in the
system.
In contrast, in a system based on explicit invocation, whenever the identity
of a that provides some system function is changed, all other modules that
import that module must also be changed.
The primary disadvantage of implicit invocation is that components
relinquish control over the computation performed by the system. When a
component announces an event, it has no idea what other components will
respond to it. Worse, even if it does know what other components are
interested in the events it announces, it cannot rely on the order in which they
are invoked. Nor can it know when they are finished. Another problem
concerns exchange of data. Sometimes data can be passed with the event. But
in other situations event systems must rely on a shared repository for
interaction. In these cases global performance and resource management can
become a serious issue. Finally, reasoning about correctness can be
problematic, since the meaning of a procedure that announces events will
Garlan & Shaw: An Introduction to Software Architecture 11
depend on the context of bindings in which it is invoked. This is in contrast to
traditional reasoning about procedure calls, which need only consider a
procedure’s pre- and post-conditions when reasoning about an invocation of it.
3.4. Layered Systems
A layered system is organized hierarchically, each layer providing service to
the layer above it and serving as a client to the layer below. In some layered
systems inner layers are hidden from all except the adjacent outer layer, except
for certain functions carefully selected for export. Thus in these systems the
components implement a virtual machine at some layer in the hierarchy. (In
other layered systems the layers may be only partially opaque.) The connectors
are defined by the protocols that determine how the layers will interact.
Topological constraints include limiting interactions to adjacent layers. Figure
3 illustrates this style.
Core
Level
Basic Utility
Useful Systems
Usersomposites ofarious elements
sually
rocecure calls
Figure 3: Layered Systems
The most widely known examples of this kind of architectural style are
layered communication protocols [31]. In this application area each layer
provides a substrate for communication at some level of abstraction. Lower
levels define lower levels of interaction, the lowest typically being defined by
hardware connections. Other appli-cation areas for this style include database
systems and operating systems [9, 32, 33 ].
Layered systems have several desirable properties. First, they support
design based on increasing levels of abstraction. This allows implementors to
partition a complex problem into a sequence of incremental steps. Second,
they support enhancement. Like pipelines, because each layer interacts with at
most the layers below and above, changes to the function of one layer affect at
most two other layers. Third, they support reuse. Like abstract data types,
different implementations of the same layer can be used interchangeably,
provided they support the same interfaces to their adjacent layers. This leads
to the possibility of defining standard layer interfaces to which different
Garlan & Shaw: An Introduction to Software Architecture 12
implementors can build. (A good example is the OSI ISO model and some of
the X Window System protocols.)
But layered systems also have disadvantages. Not all systems are easily
structured in a layered fashion. (We will see an example of this later in the
case studies.) And even if a system can logically be structured as layers,
considerations of performance may require closer coupling between logically
high-level functions and their lower-level implementations. Additionally, it
can be quite difficult to find the right levels of abstraction. This is particularly
true for standardized layered models. One notes that the communications
community has had some difficulty mapping existing protocols into the ISO
framework: many of those protocols bridge several layers.
In one sense this is similar to the benefits of implementation hiding found
in abstract data types. However, here there are multiple levels of abstraction
and implementation. They are also similar to pipelines, in that components
communicate at most with one other component on either side. But instead of
simple pipe read/write protocol of pipes, layered systems can provide much
richer forms of interaction. This makes it difficult to define system-
independent layers (as with filters)—since a layer must support the specific
protocols at its upper and lower boundaries. But it also allows much closer
interaction between layers, and permits two-way transmission of information.
3.5. Repositories
In a repository style there are two quite distinct kinds of components: a central
data structure represents the current state, and a collection of independent
components operate on the central data store. Interactions between the
repository and its external components can vary significantly between systems.
The choice of control discipline leads to major subcategories. If the types of
transactions in an input stream of transactions trigger selection of processes to
execute, the repository can be a traditional database. If the current state of the
central data structure is the main trigger of selecting processes to execute, the
repository can be a blackboard.
Figure 4 illustrates a simple view of a blackboard architecture. (We will
examine more detailed models in the case studies.) The blackboard model is
usually presented with three major parts:
The knowledge sources: separate, independent parcels of application-
dependent knowledge. Interaction among knowledge sources takes
place solely through the blackboard.
The blackboard data structure: problem-solving state data, organized into an
application-dependent hierarchy. Knowledge sources make changes to
the blackboard that lead incrementally to a solution to the problem.
Garlan & Shaw: An Introduction to Software Architecture 13
Control: driven entirely by state of blackboard. Knowledge sources respond
opportunistically when changes in the blackboard make them applicable.
Blackboard
(shared
data)
ks1 ks2
ks3
ks4
ks5ks6
ks7
ks8
omputation
M
emory
irect access
Figure 4: The Blackboard
In the diagram there is no explicit representation of the control
component. Invocation of a knowledge source is triggered by the state of the
blackboard. The actual locus of control, and hence its implementation, can be
in the knowledge sources, the blackboard, a separate module, or some
combination of these.
Blackboard systems have traditionally been used for applications requiring
complex interpretations of signal processing, such as speech and pattern
recognition. Several of these are surveyed by Nii [34]. They have also appeared
in other kinds of systems that involve shared access to data with loosely
coupled agents [35].
There are, of course, many other examples of repository systems. Batch-
sequential systems with global databases are a special case. Programming
environments are often organized as a collection of tools together with a
shared repository of programs and program fragments [36]. Even applications
that have been traditionally viewed as pipeline architectures, may be more
accurately interpreted as repository systems. For example, as we will see later,
while a compiler architecture has traditionally been presented as a pipeline, the
“phases” of most modern compilers operate on a base of shared information
(symbol tables, abstract syntax tree, etc.).
3.6. Table Driven Interpreters
In an interpreter organization a virtual machine is produced in software. An
interpreter includes the pseudo-program being interpreted and the
interpretation engine itself. The pseudo-program includes the program itself
and the interpreter’s analog of its execution state (activation record). The
interpretation engine includes both the definition of the interpreter and the
current state of its execution. Thus an interpreter generally has four
components: an interpretation engine to do the work, a memory that contains
Garlan & Shaw: An Introduction to Software Architecture 14
the pseudo-code to be interpreted, a representation of the control state of the
interpretation engine, and a representation of the current state of the program
being simulated. (See Figure 5.)
Data
(program
state)
Inputs
Outputs
Selected data
Selected instructionSimulated
Interp-
retation
Engine
Internal
I
nterpreter
State
Program
Being
Interpreted
Memory
Computation
state mach
Data access
Fetch/store
Figure 5:
Interpreter
Interpreters are commonly used to build virtual machines that close the
gap between the computing engine expected by the semantics of the program
and the computing engine available in hardware. We occasionally speak of a
programming language as providing, say, a “virtual Pascal machine.”
We will return to interpreters in more detail in the case studies.
3.7. Other Familiar Architectures
There are numerous other architectural styles and patterns. Some are
widespread and others are specific to particular domains. While a complete
treatment of these is beyond the scope of this paper, we briefly note a few of the
important categories.
• Distributed processes: Distributed systems have developed a number of
common organizations for multi-process systems [37]. Some can be
characterized primarily by their topological features, such as ring and
star organizations. Others are better characterized in terms of the kinds
of inter-process protocols that are used for communication (e.g.,
heartbeat algorithms).
One common form of distributed system architecture is a “client-server”
organization [38]. In these systems a server represents a process that provides
services to other processes (the clients). Usually the server does not know in
advance the identities or number of clients that will access it at run time. On
the other hand, clients know the identity of a server (or can find it out through
some other server) and access it by remote procedure call.
• Main program/subroutine organizations: The primary organization of
many systems mirrors the programming language in which the system
Garlan & Shaw: An Introduction to Software Architecture 15
is written. For languages without support for modularization this often
results in a system organized around a main program and a set of
subroutines. The main program acts as the driver for the subroutines,
typically providing a control loop for sequencing through the
subroutines in some order.
• Domain-specific software architectures: Recently there has been
considerable interest in developing “reference” architectures for specific
domains [39]. These architectures provide an organizational structure
tailored to a family of applications, such as avionics, command and
control, or vehicle management systems. By specializing the
architecture to the domain, it is possible to increase the descriptive
power of structures. Indeed, in many cases the architecture is sufficiently
constrained that an executable system can be generated automatically or
semi-automatically from the architectural description itself.
• State transition systems: A common organization for many reactive
systems is the state transition system [40]. These systems are defined in
terms a set of states and a set of named transitions that move a system
from one state to another.
• Process control systems: Systems intended to provide dynamic control
of a physical environment are often organized as process control systems
[41]. These systems are roughly characterized as a feedback loop in which
inputs from sensors are used by the process control system to determine
a set of outputs that will produce a new state of the environment.
3.8. Heterogeneous Architectures
Thus far we have been speaking primarily of “pure” architectural styles.
While it is important to understand the individual nature of each of these
styles, most systems typically involve some combination of several styles.
There are different ways in which architectural styles can be combined.
One way is through hierarchy. A component of a system organized in one
architectural style may have an internal structure that is developed a
completely different style. For example, in a Unix pipeline the individual
components may be represented internally using virtually any style—
including, of course, another pipe and filter, system.
What is perhaps more surprising is that connectors, too, can often be
hierarchically decomposed. For example, a pipe connector may be
implemented internally as a FIFO queue accessed by insert and remove
operations.
A second way for styles to be combined is to permit a single component to
use a mixture of architectural connectors. For example, a component might
access a repository through part of its interface, but interact through pipes with
other components in a system, and accept control information through
Garlan & Shaw: An Introduction to Software Architecture 16
another part of its interface. (In fact, Unix pipe and filter systems do this, the
file system playing the role of the repository and initialization switches playing
the role of control.)
Another example is an “active database”. This is a repository which
activates external components through implicit invocation. In this
organization external components register interest in portions of the database.
The database automatically invokes the appropriate tools based on this
association. (Blackboards are often constructed this way; knowledge sources are
associated with specific kinds of data, and are activated whenever that kind of
data is modified.)
A third way for styles to be combined is to completely elaborate one level of
architectural description in a completely different architectural style. We will
see examples of this in the case studies.
4. Case Studies
We now present six examples to illustrate how architectural principles can be
used to increase our understanding of software systems. The first example
shows how different architectural solutions to the same problem provide
different benefits. The second case study summarizes experience in developing
a a domain-specific architectural style for a family of industrial products. The
third case study examines the familiar compiler architecture in a fresh light.
The remaining three case studies present examples of the use of heterogeneous
architectures.
4.1. Case Study 1: Key Word in Context
In his paper of 1972, Parnas proposed the following problem [42]:
The KWIC [Key Word in Context] index system accepts an ordered set of
lines, each line is an ordered set of words, and each word is an ordered
set of characters. Any line may be “circularly shifted” by repeatedly
removing the first word and appending it at the end of the line. The
KWIC index system outputs a listing of all circular shifts of all lines in
alphabetical order.
Parnas used the problem to contrast different criteria for decomposing a
system into modules. He describes two solutions, one based on functional
decomposition with shared access to data representations, and a second based
on a decomposition that hides design decisions. Since its introduction, the
problem has become well-known and is widely used as a teaching device in
software engineering. Garlan, Kaiser, and Notkin also use the problem to
illustrate modularization schemes based on implicit invocation [7].
While KWIC can be implemented as a relatively small system it is not
simply of pedagogical interest. Practical instances of it are widely used by
Garlan & Shaw: An Introduction to Software Architecture 17
computer scientists. For example, the “permuted” [sic] index for the Unix Man
pages is essentially such a system.
From the point of view of software architecture, the problem derives its
appeal from the fact that it can be used to illustrate the effect of changes on
software design. Parnas shows that different problem decompositions vary
greatly in their ability to withstand design changes. Among the changes he
considers are:
• Changes in processing algorithm: For example, line shifting can be
performed on each line as it is read from the input device, on all the
lines after they are read, or on demand when the alphabetization
requires a new set of shifted lines.
• Changes in data representation: For example, lines can be stored in
various ways. Similarly, circular shifts can be stored explicitly or
implicitly (as pairs of index and offset).
Garlan, Kaiser, and Notkin, extend Parnas’ analysis by considering:
• Enhancement to system function: For example, modify the system so
that shifted lines to eliminate circular shifts that start with certain noise
words (such as “a”, “an”, “and”, etc.). Change the system to be
interactive, and allow the user to delete lines from the original (or,
alternatively, from circularly shifted) lists.
• Performance: Both space and time.
• Reuse: To what extent can the components serve as reusable entities.
We now outline four architectural designs for the KWIC system. All four
are grounded in published solutions (including implementations). The first
two are those considered in Parnas’ original article. The third solution is based
on the use of an implicit invocation style and represents a variant on the
solution examined by Garlan, Kaiser, and Notkin. The fourth is a pipeline
solution inspired by the Unix index utility.
After presenting each solution and briefly summarizing its strengths and
weakness, we contrast the different architectural decompositions in a table
organized along the five design dimensions itemized above.
Solution 1: Main Program/Subroutine with Shared Data
The first solution decomposes the problem according to the four basic
functions performed: input, shift, alphabetize, and output. These
computational components are coordinated as subroutines by a main program
that sequences through them in turn. Data is communicated between the
components through shared storage (“core storage”). Communication between
the computational components and the shared data is an unconstrained read-
Garlan & Shaw: An Introduction to Software Architecture 18
write protocol. This is made possible by the fact that the coordinating program
guarantees sequential access to the data. (See Figure 6.)
Master Control
Input Circular Shift Alphabetizer
Output
Input
Medium
Characters Index Alphabetized
Index
Output
Medium
Direct Memory Access
System I/O
Subprogram Call
Figure 6: KWIC – Shared Data Solution
Using this solution data can be represented efficiently, since computations
can share the same storage. The solution also has a certain intuitive appeal,
since distinct computational aspects are isolated in different modules.
However, as Parnas argues, it has a number of serious drawbacks in terms
of its ability to handle changes. In particular, a change in data storage format
will affect almost all of the modules. Similarly changes in the overall
processing algorithm and enhancements to system function are not easily
accomodated. Finally, this decom-position is not particularly supportive of
reuse.
Solution 2: Abstract Data Types
The second solution decomposes the system into a similar set of five modules.
However, in this case data is no longer directly shared by the computational
components. Instead, each module provides an interface that permits other
components to access data only by invoking procedures in that interface. (See
Figure 7, which illustrates how each of the components now has a set of
procedures that determine the form of access by other components in the
system.)
This solution provides the same logical decomposition into processing
modules as the first. However, it has a number of advantages over the first
solution when design changes are considered. In particular, both algorithms
and data representations can be changed in individual modules without
affecting others. Moreover, reuse is better supported than in the first solution
Garlan & Shaw: An Introduction to Software Architecture 19
because modules make fewer assumptions about the others with which they
interact.
Master Control
Input Output
Input
Medium
Output
Medium
System I/O
Subprogram Call
Characters Circular Shift
Alphabetic
Shifts
Figure 7: KWIC – Abstract Data Type Solution
On the other hand, as discussed by Garlan, Kaiser, and Notkin, the solution
is not particularly well-suited to enhancements. The main problem is that to
add new functions to the system, the implementor must either modify the
existing modules—compromising their simplicity and integrity—or add new
modules that lead to performance penalties. (See [7] for a detailed discussion.)
Solution 3: Implicit Invocation
The third solution uses a form of component integration based on shared data
similar to the first solution. However, there are two important differences.
First, the interface to the data is more abstract. Rather than exposing the
storage formats to the computing modules, data is accessed abstractly (for
example, as a list or a set). Second, computations are invoked implicitly as data
is modified. Thus interaction is based on an active data model. For example,
the act of adding a new line to the line storage causes an event to be sent to the
shift module. This allows it to produce circular shifts (in a separate abstract
shared data store). This in turn causes the alphabetizer to be implicitly
invoked so that it can alphabetize the lines.
This solution easily supports functional enhancements to the system:
additional modules can be attached to the system by registering them to be
invoked on data-changing events. Because data is accessed abstractly, it also
insulates computations from changes in data representation. Reuse is also
supported, since the implicitly invoked modules only rely on the existence of
certain externally triggered events.
Garlan & Shaw: An Introduction to Software Architecture 20
Output
Medium
Master Control
Input Output
Input
Medium
Lines
Circular
Shift
Alphabetizer
Lines
System I/O
Subprogram Call
Implicit Invocation
Figure 8: KWIC – Implicit Invocation Solution
However, the solution suffers from the fact that it can be difficult to control
the order of processing of the implicitly invoked modules. Further, because
invocations are data driven, the most natural implementations of this kind of
decomposition tend to use more space than the previously considered
decompositions.
Solution 4: Pipes and Filters
The fourth solution uses a pipeline solution. In this case there are four filters:
input, shift, alphabetize, and output. Each filter processes the data and sends it
to the next filter. Control is distributed: each filter can run whenever it has
data on which to compute. Data sharing between filters is strictly limited to
that transmitted on pipes. (See Figure 9.)
This solution has several nice properties. First, it maintains the intuitive
flow of processing. Second, it supports reuse, since each filter can function in
isolation (provided upstream filters produce data in the form it expects). New
functions are easily added to the system by inserting filters at the appropriate
point in the processing sequence. Third, it supports ease of modification, since
filters are logically independent of other filters.
Input
Output
Input
Medium
Output
Medium
Circular
Shift
Alphabetizer
System I/O
Pipe
Figure 9: KWIC – Pipe and Filter Solution
Garlan & Shaw: An Introduction to Software Architecture 21
On the other hand it has a number of drawbacks. First, it is virtually
impossible to modify the design to support an interactive system. For example,
in order to delete a line, there would have to be some persistent shared storage,
violating a basic tenet of this approach. Second, the solution is inefficient in
terms of its use of space, since each filter must copy all of the data to its output
ports.
Comparisons
The solutions can be compared by tabulating their ability to address the design
considerations itemized earlier. A detailed comparison would have to involve
consideration of a number of factors concerning the intended use of the
system: for example, is it batch or interactive, update-intensive or query-
intensive, etc.
Figure 10 provides an approximation to such an analysis, based on the
discussion of architectural styles introduced earlier. As Parnas pointed out, the
shared data solution is particularly weak in its support for changes in the
overall processing algorithm, data representations, and reuse. On the other
hand it can achieve relatively good performance, by virtue of direct sharing of
data. Further, it is relatively easy to add a new processing component (also
accessing the shared data). The abstract data type solution allows changes to
data representation and supports reuse, without necessarily compromising
performance. However, the interactions between components in that solution
are wired into the modules themselves, so changing the overall processing
algorithm or adding new functions may involve a large number of changes to
the existing system.
_
_
+
+
_
Shared
Memory ADT Events Dataflow
_
+
_
+
+
+
_
+
_
_
+
_
+
_
+
Change in Algorithm
Change in Data Repn
Change in Function
Performance
Reuse
Figure 10: KWIC – Comparison of Solutions
The implicit invocation solution is particularly good for adding new
functionality. However, it suffers from some of the problems of the shared
data approach: poor support for change in data representation and reuse.
Moreover, it may introduce extra execution overhead. The pipe and filter
solution allows new filters to be placed in the stream of text processing.
Therefore it supports changes in processing algorithm, changes in function,
Garlan & Shaw: An Introduction to Software Architecture 22
and reuse. On the other hand, decisions about data representation will be
wired into the assumptions about the kind of data that is transmitted along the
pipes. Further, depending on the exchange format, there may be additional
overhead involved in parsing and unparsing the data onto pipes.
4.2. Case Study 2: Instrumentation
Software
Our second case study describes the industrial development of a software
architecture at Tektronix, Inc. This work was carried out as a collaborative
effort between several Tektronix product divisions and the Computer
Research Laboratory over a three year period [6].
The purpose of the project was to develop a reusable system architecture
for oscilloscopes. An oscilloscope is an instrumentation system that samples
electrical signals and displays pictures (called traces) of them on a screen.
Additionally, oscilloscopes perform measurements on the signals, and also
display these on the screen. While oscilloscopes were once simple analogue
devices involving little software, modern oscilloscopes rely primarily on
digital technology and have quite complex software. It is not uncommon for a
modern oscilloscope to perform dozens of measurements, supply megabytes of
internal storage, interface to a network of workstations and other instruments,
and provide sophisticated user interface including a touch panel screen with
menus, built-in help facilities, and color displays.
Like many companies that have had to rely increasingly on software to
support their products, Tektronix was faced with number of problems. First,
there was little reuse across different oscilloscope products. Instead, different
oscilloscopes were built by different product divisions, each with their own
development conventions, software organization, programming language, and
development tools. Moreover, even within a single product division, each
new oscilloscope typically required a redesign from scratch to accommodate
changes in hardware capability and new requirements on the user interface.
This problem was compounded by the fact that both hardware and interface
requirements were changing increasingly rapidly. Furthermore, there was a
perceived need to address “specialized markets”. To do this it would have to
be possible to tailor a general-purpose instrument, to a specific set of uses.
Second, there were increasing performance problems because the software
was not rapidly configurable within the instrument. This problem arises
because an oscilloscope can be configured in many different modes, depending
on the user’s task. In old oscilloscopes reconfiguration was handled simply by
loading different software to handle the new mode. But as the total size of
software was increasing, this was leading to delays between a user’s request for
a new mode and a reconfigured instrument.
The goal of the project was to develop an architectural framework for
oscilloscopes that would address these problems. The result of that work was a
domain-specific software architecture that formed the basis of the next
Garlan & Shaw: An Introduction to Software Architecture 23
generation of Tektronix oscilloscopes. Since then the framework has been
extended and adapted to accommodate a broader class of system, while at the
same time being better adapted to the specific needs of instrumentation
software.
In the remainder of this section, we outline the stages in this architectural
development.
An object-oriented model
The first attempt at developing a reusable architecture focused on producing an
object-oriented model of the software domain. This led to a clarification of the
data types used in oscilloscopes: waveforms, signals, measurements, trigger
modes, etc. (See Figure 11.)
waveform
x-y wvfm accumulate wvfmmax-min wvfm
Oscilloscope
object
•••
Figure 11: Oscilloscopes – An Object-oriented Model
While this was a useful exercise, it fell far short of producing the hoped-for
results. Although many types of data were identified, there was no overall
model that explained how the types fit together. This led to confusion about
the partitioning of functionality. For example, should measurements be
associated with the types of data being measured, or represented externally?
Which objects should the user interface talk to?
A layered model
The second phase attempted to correct these problems by providing a layered
model of an oscilloscope. (See Figure 11.) In this model the core layer
represented the signal manipulation functions that filter signals as they enter
the oscilloscope. These functions are typically implemented in hardware. The
next layer represented waveform acquisition. Within this layer signals are
digitized and stored internally for later processing. The third layer consisted of
waveform manipulation, including measurement, waveform addition,
Fourier transformation, etc. The fourth layer consisted of display functions.
This layer was responsible for mapping digitized waveforms and
measurements to visual representations. The outermost layer was the user
interface. This layer was responsible for interacting with the user and for
deciding which data should be shown on the screen. (See Figure 12.)
Garlan & Shaw: An Introduction to Software Architecture 24
Hardware
Digitization
Visualization
User interface
Figure 12: Oscilloscopes – A Layered Model
This layered model was intuitively appealing since it partitioned the
functions of an oscilloscope into well-defined groupings. Unfortunately it was
the wrong model for the application domain. The main problem was that the
boundaries of abstraction enforced by the layers conflicted with the needs for
interaction between the various functions. For example, the model suggests
that all user interactions with an oscilloscope should be in terms of the visual
representations. But in practice real oscilloscope users need to directly affect
the functions in all layers, such as setting attenuation in the signal
manipulation layer, choosing acquisition mode and parameters in the
acquisition layer, or creating derived waveforms in the waveform
manipulation layer.
A Pipe and Filter Model
The third attempt yielded a model in which oscilloscope functions were
viewed as incremental transformers of data. Signal transformers serve to
condition external signals. Acquisition transformers derive digitized
waveforms from these signals. Display transformers convert these waveforms
into visual data. (See Figure 13.)
Trigger subsystem
Couple Acquire To-XY Clip
Measure
Signal
Waveform Trace
Measurement
Times
Figure 13: Oscilloscopes – A Pipe and Filter Model
This architectural model was a significant improvement over the layered
model in that it did not isolate the functions in separate partitions. For
example, nothing in this model would prevent signal data directly feeding into
display filters. Further, the model corresponded well to the engineers’ view of
signal processing as a dataflow problem. The main problem with the model
was that it was not clear how the user should interact with it. If the user were
simply at one end of the system, then this would represent an even worse
decomposition than the layered system.
Garlan & Shaw: An Introduction to Software Architecture 25
A Modified Pipe and Filter Model
The fourth solution accounted for user inputs by associating with each filter a
control interface that allows an external entity to set parameters of operation
for the filter. For example, the acquisition filter might have parameters that
determine sample rate and waveform duration. These inputs serve as
configuration parameters for the oscilloscope. Formally, the filters can be
modelled as “higher-order” functions, for which the configuration parameters
determine what data transformation the filter will perform. (See [17] for this
interpretation of the architecture.) Figure 14 illustrates this architecture.
Couple Acquire ClipTo-XY
Measure
Signal
Waveform Trace
Measurement
Times
Coupling Kind,Rate Trans Size
Trigger subsystem
Figure 14: Oscilloscopes – A Modified Pipe and Filter Model
The introduction of a control interface solves a large part of the user
interface problem. First, it provides a collection of settings that determine
what aspects of the oscilloscope can be modified dynamically by the user. It
also explains how changes to oscilloscope function can be accomplished by
incremental adjustments to the software. Second it decouples the signal
processing functions of the oscilloscope from the actual user interface: the
signal processing software makes no assumptions about how the user actually
communicates changes to its control parameters. Conversely, the actual user
interface can treat the signal processing functions solely in terms of the control
parameters. This allowed the designers to change the implementation of the
signal processing software and hardware without impacting an interface,
provided the control interface remained unchanged.
Further Specialization
The adapted pipe and filter model was a great improvement. But it, too, had
some problems. The most significant problem was that the pipe and filter
computational model led to poor performance. In particular, waveforms can
occupy a large amount of internal storage: it is simply not practical for each
filter to copy waveforms every time they process them. Further, different
filters may run at radically different speeds: it is unacceptable to slow one filter
down because another filter is still processing its data.
To handle these problems the model was further specialized. Instead of
having a single kind of pipe, several “colors” of pipes were introduced. Some
of these allowed data to be processed without copying. Others permitted data to
be ignored by slow filters if they were in the middle of processing other data.
These additional pipes increased the stylistic vocabulary and allowed the
Garlan & Shaw: An Introduction to Software Architecture 26
pipe/filter computations to be tailored more directly to the performance needs
of the product.
Summary
This case study illustrates the issues involved in developing an architectural
style for a real application domain. It underscores the fact that different
architectural styles have different effects on the ability to solve a set of
problems. Moreover, it illustrates that architectural designs for industrial
software must typically be adapted from pure forms to specialized styles that
meet the needs of the specific domain. In this case, the final result depended
greatly on the properties of pipe and filter architectures, but found ways to
adapt that generic style so that it could also satisfy the performance needs of the
product family.
4.3. Case 3: A Fresh View of Compilers
The architecture of a system can change in response to improvements in
technology. This can be seen in the way we think about compilers.
In the 1970s, compilation was regarded as a sequential process, and the
organization of a compiler was typically drawn as in Figure 15. Text enters at
the left end and is transformed in a variety of ways—to lexical token stream,
parse tree, intermediate code—before emerging as machine code on the right.
We often refer to this compilation model as a pipeline, even though it was (at
least originally) closer to a batch sequential architecture in which each
transformation (“pass”) was completed before the next one started.
Lex Syn Sem Opt
Code
Text Code
Figure 15: Traditional Compiler Model
In fact, even the batch sequential version of this model was not completely
accurate. Most compilers created a separate symbol table during lexical analysis
and used or updated it during subsequent passes. It was not part of the data
that flowed from one pass to another but rather existed outside all the passes.
So the system structure was more properly drawn as in Figure 16.
SymTab
Lex Syn Sem Opt Code
Text Code
Figure 16: Traditional Compiler Model with Shared Symbol Table
As time passed, compiler technology grew more sophisticated. The
algorithms and representations of compilation grew more complex, and
increasing attention turned to the intermediate representation of the program
Garlan & Shaw: An Introduction to Software Architecture 27
during compilation. Improved theoretical understanding, such as attribute
grammers, accelerated this trend. The consequence was that by the mid-1980s
the intermediate representation (for example, an attributed parse tree), was the
center of attention. It was created early during compilation and manipulated
during the remainder; the data structure might change in detail, but it
remained substantially one growing structure throughout. However, we
continued (sometimes to the present) to model the compiler with sequential
data flow as in Figure 17.
Lex Syn Sem Opt Code
SymTab
Tree
Text Code
Computations
(transducers and
transforms)
Memory
Data fetch/store
Vestigal data flow
Figure 17: Modern Canonical Compiler
In fact, a more appropriate view of this structure would re-direct attention
from the sequence of passes to the central shared representation. When you
declare that the tree is the locus of compilation information and the passes
define operations on the tree, it becomes natural to re-draw the architecture as
in Figure 18. Now the connections between passes denote control flow, which
is a more accurate depiction; the rather stronger connections between the
passes and the tree/symbol table structure denote data access and
manipulation. In this fashion, the architecture has become a repository, and
that is indeed a more appropriate way to think about a compiler of this class.
Happily, this new view also accommodates various tools that operate on
the internal representation rather than the textual form of a program; these
include syntax-directed editors and various analysis tools.
SymTab
TreeLex
Syn
Sem
Opt2
Code
Edit Syn
Might be
rule-basedOpt1
Figure 18: Canonical Compiler, Revisited
Garlan & Shaw: An Introduction to Software Architecture 28
Note that this repository resembles a blackboard in some respects and
differs in others. Like a blackboard, the information of the computation is
located centrally and operated on by independent computations which interact
only through the shared data. However, whereas the execution order of the
operations in a blackboard is determined by the types of the incoming database
modifications, the execution order of the compiler is predetermined.
4.4. Case 4: A Layered Design with Different Styles for the Layers
The PROVOX® system by Fisher Controls offers distributed process control for
chemical production processes [43]. Process control capabilities range from
simple control loops that control pressure, flow, or levels to complex strategies
involving interrelated control loops. Provisions are made for integration with
plant management and information systems in support of computer integrated
manufacturing. The system architecture integrates process control with plant
management and information systems in a 5-level layered hierarchy. Figure
19 shows this hierarchy: the right side is the software view, and the left side is
the hardware view.
PROVOXplus Controllers
PROVUE Consoles
ENVOX Configuration
PROVOXplus
Application Software
CorpComputers
Interface to
Host Computers
Pro
ces
s M
eas
ure
me
nt
and
Co
ntro
l
Pro
ces
s
Su
per
vis
ion
Pro
ces
s
Ma
nag
em
ent
Co
rp
Mg
mt.
Pla
nt
Ma
nag
em
ent
Level 5
Level 4
Level 3
Level 2
Level 1
Software
Figure 19: PROVOX – Hierarchical Top Level
Each level corresponds to a different process management function with its
own decision-support requirements.
• Level 1: Process measurement and control: direct adjustment of final
control elements.
• Level 2: Process supervision: operations console for monitoring and
controlling Level 1.
Garlan & Shaw: An Introduction to Software Architecture 29
• Level 3: Process management: computer-based plant automation,
including management reports, optimization strategies, and guidance to
operations console.
• Levels 4 and 5: Plant and corporate management: higher-level
functions such as cost accounting, inventory control, and order
processing/scheduling.
Different kinds of computation and response times are required at different
levels of this hierarchy. Accordingly, different computational models are used.
Levels 1 to 3 are object-oriented; Levels 4 and 5 are largely based on
conventional data-processing repository models. For present purposes it
suffices to examine the object-oriented model of Level 2 and the repositories of
Levels 4 and 5.
T
AG
DATA ACTION ERVICES
O
PERATING
T
UNING
C
ONFIGURATION
LGORITHM
AME(S)
EMPLATE
AG
ATA CTION SERVICES
OPERATING
TUNING
CONFIGURATION
ALGORITHM
NAME(S)
TEMPLATE
TAG
DATA ACTION SERVICES
OPERATING
TUNING
CONFIGURATION
ALGORITHM
NAME(S)
TEMPLATE
TAG
DATA ACTION SERVICES
PERATING
UNING
ONFIGURATION
COMMUNICATION
TRACE
ALARMS
ETC
ALGORITHM
NAME(S)
TEMPLATE
Figure 20: PROVOX – Object-oriented Elaboration
For the control and monitoring functions of Level 2, PROVOX uses a set of
points, or loci of process control. Figure 20 shows the canonical form of a point
definition; seven specialized forms support the most common kinds of
control. Points are, in essence, object-oriented design elements that
encapsulate information about control points of the process. The points are
individually configured to achieve the desired control strategy. Data associated
with a point includes: Operating parameters, including current process value,
setpoint (target value), valve output, and mode (automatic or manual).
Tuning parameters, such as gain, reset, derivative, and alarm trip-points.
Configuration parameters, including tag (name) and I/O channels.
In addition, the point’s data can include a template for a control strategy.
Like any good object, a point also includes procedural definitions such as
control algorithms, communication connections, reporting services, and trace
facilities. A collection of points implements the desired process control
strategy through the communication services and through the actual dynamics
Garlan & Shaw: An Introduction to Software Architecture 30
of the process (e.g., if one point increases flow into a tank, the current value of
a point that senses tank level will reflect this change). Although the
communication through process state deviates from the usual procedural or
message-based control of objects, points are conceptually very like objects in
their encapsulation of essential state and action information.
Reports from points appear as input transactions to data collection and
analysis processes at higher design levels. The organization of the points into
control processes can be defined by the designer to match the process control
strategy. These can be further aggregated into Plant Process Areas (points
related to a set of equipment such as a cooling tower) and thence into Plant
Management Areas (segments of a plant that would be controlled by single
operators).
PROVOX makes provisions for integration with plant management and
business systems at Levels 4 and 5. Selection of those systems is often
independent of process control design; PROVOX does not itself provide MIS
systems directly but does provide for integrating a conventional host computer
with conventional database management. The data collection facilities of
Level 3, the reporting facilities of Level 2, and the network that supports
distributed implementation suffice to provide process information as
transactions to these databases. Such databases are commonly designed as
repositories, with transaction processing functions supporting a central data
store—quite a different style from the object-oriented design of Level 2.
The use of hierarchical layers at the top level of a system is fairly common.
This permits strong separation of different classes of function and clean
interfaces between the layers. However, within each layer the interactions
among components are often too intricate to permit strict layering.
4.5. Case 5: An Interpreter Using Different Idioms for the Components
Rule-based systems provide a means of codifying the problem-solving know-
how of human experts. These experts tend to capture problem-solving
techniques as sets of situation-action rules whose execution or activation is
sequenced in response to the conditions of the computation rather than by a
predetermined scheme. Since these rules are not directly executable by
available computers, systems for interpreting such rules must be provided.
Hayes-Roth surveyed the architecture and operation of rule-based systems [44].
The basic features of a rule-based system, shown in Hayes-Roth’s rendering
as Figure 21, are essentially the features of a table-driven interpreter, as
outlined earlier.
• The pseudo-code to be executed, in this case the knowledge base
• The interpretation engine, in this case the rule interpreter, the heart of
the inference engine
Garlan & Shaw: An Introduction to Software Architecture 31
• The control state of the interpretation engine, in this case the rule and
data element selector
• The current state of the program running on the virtual machine, in
this case the working memory.
Rule
Base
Fact
Memory
Knowledge Base
Working
Memory
Inputs
Outputs
Selected data
Selected ruleRule
Interpreter
Rule and
Data Element
Selection
emory
omputation
tate mach
Figure 21: Basic Rule-Based System
Rule-based systems make heavy use of pattern matching and context
(currently relevant rules). Adding special mechanisms for these facilities to
the design leads to the more complicated view shown in Figure 22. In adding
this complexity, the original simple interpreter vanishes in a sea of new
interactions and data flows. Although the interfaces among the original
modules remain, they are not distinguished from the newly-added interfaces.
However, the interpreter model can be rediscovered by identifying the
components of Figure 22 with their design antecedents in Figure 21. This is
done in Figure 23. Viewed in this way, the elaboration of the design becomes
much easier to explain and understand. For example, we see that:
• The knowledge base remains a relatively simple memory structure,
merely gaining substructure to distinguish active from inactive
contents.
• The rule interpreter is expanded with the interpreter idiom (that is, the
interpretation engine of the rule-based system is itself implemented as a
table-driven interpreter), with control procedures playing the role of the
pseudo-code to be executed and the execution stack the role of the
current program state.
• “Rule and data element selection” is implemented primarily as a
pipeline that progressively transforms active rules and facts to
prioritized activations; in this pipeline the third filter (“nominators”)
also uses a fixed database of metarules.
• Working memory is not further elaborated.
Garlan & Shaw: An Introduction to Software Architecture 32
Inactive
rules
Active
rules
Active
facts
Inactive
facts
Rule and
fact
compiler
Rule
antecedent
subexpressions
Rule and
fact
compiler
Agenda
Scheduler
nterpreter
Active
rules
and
facts
Activation/
deactivation
etarules
Matching
pairs
andidate
rule, data>
Preferences
and
priorities
Prioritized
activations
Selected
action
Incomplete
procedures
Multi-
dimensional
working
memory
Updatesata
xecution
tack
Control
procedures
Next action
Unfinished
actions
Inputs
Outputs
Triggering
data
activations
Delete
completed
activations
ule Memoryule Memoryule Memoryule Memoryule Memoryule Memoryule Memory Fact Memory
Data-flow
network
by partially
evaluated
rule activations
Figure 23: Sophisticated Rule-Based System
The interfaces among the rediscovered components are unchanged from
the simple model except for the two bold lines over which the interpreter
controls activations.
This example illustrates two points. First, in a sophisticated rule-based
system the elements of the simple rule-based system are elaborated in response
to execution characteristics of the particular class of languages being
interpreted. If the design is presented in this way, the original concept is
retained to guide understanding and later maintenance. Second, as the design
is elaborated, different components of the simple model can be elaborated with
different idioms.
Garlan & Shaw: An Introduction to Software Architecture 33
Inactive
rules
Active
rules
Active
facts
Inactive
facts
Rule and
fact
compiler
Rule
antecedent
subexpressions
Rule and
fact
compiler
AgendaScheduler
Interpreter
Active
rules
and
facts
Activation/
deactivation
Metarules
Matching
pairs
Candidate
Preferences
and
priorities
Prioritized
activations
Selected
action
Incomplete
procedures
Multi-
dimensional
working
memory
UpdatesData
Execution
stack
Control
procedures
Next action
Unfinished
actions
Inputs
Outputs
Triggering
data
activations
Delete
completed
activations
Working
Memory
Knowledge
Base
Rule Memory Fact Memory
Rule and data
element
selection
Rule Interpreter
Data-flow
network
by partially
evaluated
rule activations
Figure 23: Simplified Sophisticated Rule-Based System
Note that the rule-based model is itself a design structure: it calls for a set of
rules whose control relations are determined during execution by the state of
the computation. A rule-based system provides a virtual machine—a rule
executor—to support this model.
4.6. Case 6: A Blackboard Globally Recast as Interpreter
The blackboard model of problem solving is a highly structured special case of
opportunistic problem solving. In this model, the solution space is organized
into several application-dependent hierarchies and the domain knowledge is
Garlan & Shaw: An Introduction to Software Architecture 34
partitioned into independent modules of knowledge that operate on
knowledge within and between levels [34]. Figure 4 showed the basic
architecture of a blackboard system and outlined its three major parts:
knowledge sources, the blackboard data structure, and control.
(
Control flow—–Data flow)
Level nLevel n
Condition
Action
Condition
Blackboard
Monitor
Scheduling
Queue
Focus of
Control
Database
Scheduler
Blackboard
Knowledge Sources
Blackboard
Change
Condition Part
Level 3
Level 2
Level 1
Action
Condition
Action
Stimulus
Response Frame
Figure 24: Hearsay-II
The first major blackboard system was the HEARSAY-II speech recognition
system. Nii’s schematic of the HEARSAY-II architecture appears as Figure 24.
The blackboard structure is a six- to eight-level hierarchy in which each level
abstracts information on its adjacent lower level and blackboard elements rep-
resent hypotheses about the interpretation of an utterance. Knowledge sources
correspond to such tasks as segmenting the raw signal, identifying phonemes,
generating word candidates, hypothesizing syntactic segments, and proposing
semantic interpretations. Each knowledge source is organized as a condition
part that specifies when it is applicable and an action part that processes
relevant blackboard elements and generates new ones. The control component
is realized as a blackboard monitor and a scheduler; the scheduler monitors the
blackboard and calculates priorities for applying the knowledge sources to
various elements on the blackboard.
HEARSAY-II was implemented between 1971 and 1976 on DEC PDP-10s;
these machines were not directly capable of condition-triggered control, so it
should not be surprising to find that an implementation provides the
mechanisms of a virtual machine that realizes the implicit invocation
semantics required by the blackboard model.
Garlan & Shaw: An Introduction to Software Architecture 35
Control flow—–Data flow)
Level nLevel n
Condition
Action
Condition
Blackboard
Monitor
Scheduling
Queue
ocus of
ontrol
atabase
Scheduler
Blackboard
Knowledge Sources
Blackboard
Change
Condition Part
Level 3
Level 2
Level 1
Action
Condition
Action
Stimulus
Response Frame
KS
KS
KS
Blackboard
Not
relevant to
blackboard
model
Figure 25: Blackboard View of Hearsay-II
Figure 24 not only elaborates the individual components of Figure 4; it also
adds components for the previously-implicit control component. In the
process, the figure becomes rather complex. This complexity arises because it is
now illustrating two concepts: the blackboard model and realization of that
model by a virtual machine. The blackboard model can be recovered as in
Figure 25 by suppressing the control mechanism and regrouping the
conditions and actions into knowledge sources.
The virtual machine can be seen to be realized by an interpreter using the
assignment of function in Figure 26. Here the blackboard cleanly corresponds
to the current state of the recognition task. The collection of knowledge
sources roughly supplies the pseudocode of the interpreter; however, the
actions also contribute to the interpretation engine. The interpretation engine
includes several components that appear explicitly in Figure 24: the blackboard
monitor, the focus of control database, and the scheduler, but also the actions
of the knowledge sources. The scheduling queue corresponds roughly to the
control state. To the extent that execution of conditions determines priorities,
the conditions contribute to rule selection as well as forming pseudocode.
Here we see a system initially designed with one model (blackboard, a
special form of repository), then realized through a different model
(interpreter). The realization is not a component-by-component expansion as
Garlan & Shaw: An Introduction to Software Architecture 36
in the previous two examples; the view as an interpreter is a different
aggregation of components from the view as blackboard.
( Control flow—–Data flow)
Level nLevel n
Condition
Action
Condition
Blackboard
Monitor
Scheduling
Queue
Focus of
Control
Database
Scheduler
Blackboard
Knowledge Sources
Blackboard
Change
Condition Part
Level 3
Level 2
Level 1
Action
Condition
Action
Stimulus
Response Frame
Program State
(Working Meomory)
Interpretation
Engine
Pseudo-Code
(Knowledge Base)
N
ot
e:
A
ct
io
n
s
ar
e
al
so
p
ar
t
of
in
te
rp
re
ta
ti
on
e
n
gi
n
e
Control
State
Figure 26: Interpreter View of Hearsay-II
5. Past, Present, and Future
We have outlined a number of architectural styles and shown how they can be
applied and adapted to specific software systems. We hope that this has
convinced the reader that analysis and design of systems in terms of software
architecture is both viable and worth doing. Further we hope to have made it
clear that an understanding of the emerging vocabulary of architectural styles
is a significant—if not necessary—intellectual tool for the software engineer.
There is, of course, much more to software architecture than we have had
space to cover. In particular, we have said very little about existing results in
the areas of analysis, formal specification, domain-specific architectures,
module interconnection languages, and special-architecture tools.
This is not to say that more work isn’t needed. Indeed, we can expect to see
significant advances in a number of areas including:
• Better taxonomies of architectures and architectural styles.
Garlan & Shaw: An Introduction to Software Architecture 37
• Formal models for characterizing and analyzing architectures.
• Better understanding of the primitive semantic entities from which
these styles are composed.
• Notations for describing architectural designs.
• Tools and environments for developing architectural designs.
• Techniques for extracting architectural information from existing code.
• Better understanding of the role of architectures in the life-cycle process.
These are all areas of active research both in industry and academia. Given
the increasing interest in this emerging field, we can expect that our
understanding of the principles and practice of software architecture will
improve considerably over time. However, as we have illustrated, even with
the basic concepts that we now have in hand, design at the level of software
architecture can provide direct and substantial benefit to the practice of
software engineering.
Acknowledgements
We gratefully acknowledge our many colleagues who have contributed to
the ideas presented in this paper. In particular, we would like to thank Chris
Okasaki, Curtis Scott, and Roy Swonger for their help in developing the course
from which much of this material was drawn. We thank David Notkin, Kevin
Sullivan, and Gail Kaiser for their contribution towards understanding event-
based systems. Rob Allen helped develop a rigorous understanding of the pipe
and filter style. We would like to acknowledge the oscilloscope development
team at Tektronix, and especially Norm Delisle, for their part in demonstrating
the value of domain-specific architectural styles in an industrial context.
Finally, we would like to thank Barry Boehm, Larry Druffel, and Dilip Soni for
their constructive comments on early drafts of the paper.
Bibliography
[1] D. Garlan, M. Shaw, C. Okasaki, C. Scott, and R. Swonger, “Experience
with a course on architectures for software systems,” in Proceedings of the Sixth SEI
Conference on Software Engineering Education, Springer-Verlag, LNCS 376, October 1992.
[2] M. Shaw, “Toward higher-level abstractions for software systems,” in Data & Knowledge
Engineering, vol. 5, pp. 119-128, North Holland: Elsevier Science Publishers B.V., 1990.
[3] M. Shaw, “Heterogeneous design idioms for software architecture,” in Proceedings of the
Sixth International Workshop on Software Specification and Design, IEEE Computer
Society, Software Engineering Notes, (Como, Italy), pp. 158-165, October 25-26, 1991.
[4] M. Shaw, “Software architectures for shared information systems,” in Mind Matters:
Contributions to Cognitive and Computer Science in Honor of Allen Newell, Erlbaum, 1993.
[5] R. Allen and D. Garlan, “A formal approach to software architectures,” in Proceedings of
IFIP’92 (J. van Leeuwen, ed.), Elsevier Science Publishers B.V., September 1992.
Garlan & Shaw: An Introduction to Software Architecture 38
[6] D. Garlan and D. Notkin, “Formalizing design spaces: Implicit invocation mechanisms,” in
VDM’91: Formal Software Development Methods. (Noordwijkerhout, The Netherlands),
pp. 31-44, Springer-Verlag, LNCS 551, October 1991.
[7] D. Garlan, G. E. Kaiser, and D. Notkin, “Using tool abstraction to compose systems,” IEEE
Computer, vol. 25, June 1992.
[8] A. Z. Spector et al., “Camelot: A distributed transaction facility for Mach and the Internet –
an interim report,” Tech. Rep. CMU-CS-87-129, Carnegie Mellon University, June 1987.
[9] M. Fridrich and W. Older, “Helix: The architecture of the XMS distributed file system,”
IEEE Software, vol. 2, pp. 21-29, May 1985.
[10] M. A. Linton, “Distributed management of a software database,” IEEE Software, vol. 4, pp.
70-76, November 1987.
[11] V. Seshadri et al., “Sematic analysis in a concurrent compiler,” in Proceedings of ACM
SIGPLAN ’88 Conference on Programming Language Design and Implementation, ACM
SIGPLAN Notices, 1988.
[12] M. C. Paulk, “The ARC Network: A case study,” IEEE Software, vol. 2, pp. 61-69, May 1985.
[13] M. Chen and R. J. Norman, “A framework for integrated case,” IEEE Software, vol. 9, pp. 18-
22, March 1992.
[14] NIST/ECMA reference model for frameworks of software engineering environments.” NIST
Special Publication 500-201, December 1991.
[15] R. W. Scheifler and J. Gettys, “The X window system,” AACM Transactions on Graphics, vol.
5, pp. 79-109, April 1986.
[16] M. J. Bach, The Design of the UNIX Operating System, ch. 5.12, pp. 111-119. Software
Series, Prentice-Hall, 1986.
[17] N. Delisle and D. Garlan, “Applying formal specification to industrial problems: A
specification of an oscilloscope,” IEEE Software, September 1990.
[18] G. Kahn, “The semantics of a simple language for parallel programming,” Information
Processing, 1974.
[19] M. R. Barbacci, C. B. Weinstock, and J. M. Wing, “Programming at the processor-memory-
switch level,” in Proceedings of the 10th International Conference on Software Engineering,
(Singapore), pp. 19-28, IEEE Computer Society Press, April 1988.
[20] G. E. Kaiser and D. Garlan, “Synthesizing programming environments from reusable
features,” in Software Reusability (T. J. Biggerstaff and A. J. Perlis, eds.), vol. 2, ACM Press,
1989.
[21] W. Harrison, “RPDE: A framework for integrating tool fragments,” IEEE Software, vol. 4,
November 1987.
[22] C. Hewitt, “Planner: A language for proving theorems in robots,” in Proceedings of the First
International Joint Conference in Artificial Intelligence, 1969.
[23] S. P. Reiss, “Connecting tools using message passing in the field program development
environment,” IEEE Software, July 1990.
[24] C. Gerety, “HP Softbench: A new generation of software development tools,” Tech. Rep.
SESD-89-25, Hewlett-Packard Software Engineering Systems Division, Fort Collins,
Colorado, November 1989.
[25] R. M. Balzer, “Living with the next generation operating system,” in Proceedings of the 4th
World Computer Conference, September 1986.
Garlan & Shaw: An Introduction to Software Architecture 39
[26] G. Krasner and S. Pope, “A cookbook for using the model-view-controller user interface
paradigm in Smalltalk-80,” Journal of Object Oriented Programming, vol. 1, pp. 26-49,
August/September 1988.
[27] M. Shaw, E. Borison, M. Horowitz, T. Lane, D. Nichols, and R. Pausch, “Descartes: A
programming-language approach to interactive display interfaces,” Proceedings of
SIGPLAN ’83: Symposium on Programming Language Issues in Software Systems, ACM
SIGPLAN Notices, vol. 18, pp. 100-111, June 1983.
[28] A. N. Habermann and D. S. Notkin, “Gandalf: Software development environments,” IEEE
Transactions on Software Engineering, vol. SE-12, pp. 1117-1127, December 1986.
[29] A. N. Habermann, D. Garlan, and D. Notkin, “Generation of integrated task-specific
software environments,” in CMU Computer Science: A 25th Commemorative (R. F. Rashid,
ed.), Anthology Series, pp. 69-98, ACM Press, 1991.
[30] K. Sullivan and D. Notkin, “Reconciling environment integration and component
independence,” in Proceedings of ACM SIGSOFT90: Fourth Symposium on Software
Development Environments, pp. 22-23, December 1990.
[31] G. R. McClain, ed., Open Systems Interconnection Handbook. New York, NY: Intertext
Publications McGraw-Hill Book Company, 1991.
[32] D. Batory and S. O’Malley, “The design and implementation of hierarchical software
systems using reusable components,” Tech. Rep. TR-91-22, Department of Computer Science,
University of Texas, Austin, June 1991.
[33] H. C. Lauer and E. H. Satterthwaite, “Impact of MESA on system design,” in Proceedings of
the Third International Conference on Software Engineering, (Atlanta, GA), pp. 174-175,
IEEE Computer Society Press, May 1979.
[34] H. P. Nii, “Blackboard systems Parts 1 & 2,” AI Magazine, vol. 7 nos 3 (pp. 38-53) and 4 (pp.
62-69), 1986.
[35] V. Ambriola, P. Ciancarini, and C. Montangero, “Software process enactment in oikos,” in
Proceedings of the Fourth ACM SIGSOFT Symposium on Software Development
Environments, SIGSOFT Software Engineering Notes, (Irvine, CA), pp. 183-192, December
1990.
[36] D. R. Barstow, H. E. Shrobe, and E. Sandewall, eds., Interactive Programming Environments.
McGraw-Hill Book Co., 1984.
[37] G. R. Andrews, “Paradigms for process interaction in distributed programs,” ACM Computing
Surveys, vol. 23, pp. 49-90, March 1991.
[38] A. Berson, Client/Server Architecture. McGraw-Hill, 1992.
[39] E. Mettala and M. H. Graham, eds., The Domain-Specific Software Architecture Program.
No. CMU/SEI-92-SR-9, Carnegie Mellon Software Engineering Institute, June 1992.
[40] D. Harel, “Statecharts: A visual formalism for complex systems,” Science of Computer
Programming, vol. 8, pp. 231-274, 1987.
[41] K. J. Åström and B. Wittenmark, Computer-Controlled Systems Design. Prentice Hall,
second ed., 1990.
[42] D. L. Parnas, “On the criteria to be used in decomposing systems into modules,”
Communications of the ACM, vol. 15, pp. 1053-1058, December 1972.
[43] “PROVOX plus Instrumentation System: System overview,” 1989.
[44] F. Hayes-Roth, ‘Rule-based systems,” Communications of the ACM, vol. 28, pp. 921-932,
September 1985.
An Introduction to Software Architectur
e
David Garlan and Mary Shaw
January 1994
CMU-CS-94-166
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213-3890
Also published as “An Introduction to Software Architecture,” Advances in Software Engineering
and Knowledge Engineering, Volume I, edited by V.Ambriola and G.Tortora, World Scientific
Publishing Company, New Jersey, 1993
.
Also appears as CMU Software Engineering Institute Technical Report
CMU/SEI-94-TR-21, ESC-TR-94-21.
©1994 by David Garlan and Mary Shaw
This work was funded in part by the Department of Defense Advanced Research Project Agency under gra
nt
MDA972-92-J-1002, by National Science Foundation Grants CCR-9109469 and CCR-9112880, and by a grant
from Siemens Corporate Research. It was also funded in part by the Carnegie Mellon University School of
Computer Science and Software Engineering Institute (which is sponsored by the U.S. Department of Defense).
The views and conclusions contained in this document are those of the authors and should not be interpreted
as representing the official policies, either expressed or implied, of the U.S. Government, the Department of
Defense, the National Science Foundation, Siemens Corporation, or Carnegie Mellon University.
Keywords: Software architecture, software design, software engineering
Abstract
As the size of software systems increases, the algorithms and data structures of
the computation no longer constitute the major design problems. When
systems are constructed from many components, the organization of the
overall system—the software architecture—presents a new set of design
problems. This level of design has been addressed in a number of way
s
including informal diagrams and descriptive terms, module interconnection
languages, templates and frameworks for systems that serve the needs of
specific domains, and formal models of component integration mechanisms.
In this paper we provide an introduction to the emerging field of software
architecture. We begin by considering a number of common architectural
styles upon which many systems are currently based and show how different
styles can be combined in a single design. Then we present six case studies to
illustrate how architectural representations can improve our understanding of
complex software systems. Finally, we survey some of the outstanding
problems in the field, and consider a few of the promising research directions.
Contents
1. Introduction ……………………………………………………………………………………….. 2
2. From Programming Languages to Software Architecture ………………… 3
2.1. High-level Programming Languages …………………………………………………………. 3
2.2. Abstract Data Types………………………………………………………………………………. 4
2.3. Software Architecture ……………………………………………………………………………. 4
3. Common Architectural Styles……………………………………………………………. 5
3.1. Pipes and Filters …………………………………………………………………………………… 6
3.2. Data Abstraction and Object-Oriented Organization ……………………………………. 8
3.3. Event-based, Implicit Invocation ……………………………………………………………… 9
3.4. Layered Systems …………………………………………………………………………………… 11
3.5. Repositories …………………………………………………………………………………………. 12
3.6. Table Driven Interpreters ……………………………………………………………………….. 13
3.7. Other Familiar Architectures………………………………………………………………….. 14
3.8. Heterogeneous Architectures……………………………………………………………………. 15
4. Case Studies ………………………………………………………………………………………… 16
4.1. Case Study 1: Key Word in Context …………………………………………………………… 16
4.2. Case Study 2: Instrumentation Software …………………………………………………….. 22
4.3. Case 3: A Fresh View of Compilers …………………………………………………………… 26
4.4. Case 4: A Layered Design with Different Styles for the Layers ……………………… 28
4.5. Case 5: An Interpreter Using Different Idioms for the Components …………………… 30
4.6. Case 6: A Blackboard Globally Recast as Interpreter ……………………………………. 33
5. Past, Present, and Future ……………………………………………………………………. 36
Acknowledgements ………………………………………………………………………………………. 37
Bibliography ………………………………………………………………………………………………….. 37
Garlan & Shaw: An Introduction to Software Architecture 1
List of Figures
1 Pipes and Filters …………………………………………………………………………………………. 7
2 Abstract Data Types and Objects…………………………………………………………………. 8
3 Layered Systems ……………………………………………………………………………………….. 11
4 The Blackboard …………………………………………………………………………………………. 13
5 Interpreter…………………………………………………………………………………………………. 14
6 KWIC – Shared Data Solution ………………………………………………………………….. 18
7 KWIC – Abstract Data Type Solution ……………………………………………………….. 19
8 KWIC – Implicit Invocation Solution ……………………………………………………… 20
9 KWIC – Pipe and Filter Solution ……………………………………………………………… 20
10 KWIC – Comparison of Solutions ……………………………………………………………. 21
11 Oscilloscopes – An Object-oriented Model……………………………………………….. 23
12 Oscilloscopes – A Layered Model ……………………………………………………………… 24
13 Oscilloscopes – A Pipe and Filter Model…………………………………………………… 24
14 Oscilloscopes – A Modified Pipe and Filter Model…………………………………… 25
15 Traditional Compiler Model ……………………………………………………………………. 26
16 Traditional Compiler Model with Shared Symbol Table ……………………….. 26
17 Modern Canonical Compiler …………………………………………………………………… 27
18 Canonical Compiler, Revisited………………………………………………………………… 27
19 PROVOX – Hierarchical Top Level…………………………………………………………… 28
20 PROVOX – Object-oriented Elaboration …………………………………………………… 29
21 Basic Rule-Based System ………………………………………………………………………….. 31
22 Sophistocated Rule-Based System……………………………………………………………. 32
23 Simplified Rule-Based System…………………………………………………………………. 33
24 Hearsay-II ………………………………………………………………………………………………….. 34
25 Blackboard View of Hearsay-II …………………………………………………………………. 35
26 Interpreter View of Hearsay-II …………………………………………………………………. 36
Garlan & Shaw: An Introduction to Software Architecture 2
1. Introduction
As the size and complexity of software systems increases, the design problem
goes beyond the algorithms and data structures of the computation: designing
and specifying the overall system structure emerges as a new kind of problem.
Structural issues include gross organization and global control structure;
protocols for communication, synchronization, and data access; assignment of
functionality to design elements; physical distribution; composition of design
elements; scaling and performance; and selection among design alternatives.
This is the software architecture level of design. There is a considerable
body of work on this topic, including module interconnection languages,
templates and frameworks for systems that serve the needs of specific domains,
and formal models of component integration mechanisms. In addition, an
implicit body of work exists in the form of descriptive terms used informally to
describe systems. And while there is not currently a well-defined terminology
or notation to characterize architectural structures, good software engineers
make common use of architectural principles when designing complex
software. Many of the principles represent rules of thumb or idiomatic
patterns that have emerged informally over time. Others are more carefully
documented as industry and scientific standards.
It is increasingly clear that effective software engineering requires facility in
architectural software design. First, it is important to be able to recognize
common paradigms so that high-level relationships among systems can be
understood and so that new systems can be built as variations on old systems.
Second, getting the right architecture is often crucial to the success of a software
system design; the wrong one can lead to disastrous results. Third, detailed
understanding of software architectures allows the engineer to make
principled choices among design alternatives. Fourth, an architectural system
representation is often essential to the analysis and description of the high-
level properties of a complex system.
In this paper we provide an introduction to the field of software
architecture. The purpose is to illustrate the current state of the discipline and
examine the ways in which architectural design can impact software design.
The material presented here is selected from a semester course, Architectures
for Software Systems, taught at CMU by the authors [1]. Naturally, a short
paper such as this can only briefly highlight the main features of the terrain.
This selection emphasizes informal descriptions omitting much of the course’s
material on specification, evaluation, and selection among design alternatives.
We hope, nonetheless, that this will serve to illuminate the nature and
significance of this emerging field.
In the following section we outline a number of common architectural
styles upon which many systems are currently based, and show how
Garlan & Shaw: An Introduction to Software Architecture 3
heterogeneous styles can be combined in a single design. Next we use six case
studies to illustrate how architectural representations of a software system can
improve our understanding of complex systems. Finally, we survey some of
the outstanding problems in the field, and consider a few of the promising
research directions.
The text that makes up the bulk of this article has been drawn from
numerous other publications by the authors. The taxonomy of architectural
styles and the case studies have incorporated parts of several published papers
[1, 2, 3, 4]. To a lesser extent material has been drawn from other articles by the
authors [5, 6, 7].
2. From Programming Languages to Software Architecture
One characterization of progress in programming languages and tools has been
regular increases in abstraction level—or the conceptual size of software
designers building blocks. To place the field of Software Architecture into
perspective let us begin by looking at the historical development of abstraction
techniques in computer science.
2.1. High-level Programming Languages
When digital computers emerged in the 1950s, software was written in
machine language; programmers placed instructions and data individually and
explicitly in the computer’s memory. Insertion of a new instruction in a
program might require hand-checking of the entire program to update
references to data and instructions that moved as a result of the insertion.
Eventually it was recognized that the memory layout and update of references
could be automated, and also that symbolic names could be used for operation
codes, and memory addresses. Symbolic assemblers were the result. They were
soon followed by macro processors, which allowed a single symbol to stand for
a commonly-used sequence of instructions. The substitution of simple
symbols for machine operation codes, machine addresses yet to be defined, and
sequences of instructions was perhaps the earliest form of abstraction in
software.
In the latter part of the 1950s, it became clear that certain patterns of
execution were commonly useful—indeed, they were so well understood that
it was possible to create them automatically from a notation more like
mathematics than machine language. The first of these patterns were for
evaluation of arithmetic expressions, for procedure invocation, and for loops
and conditional statements. These insights were captured in a series of early
high-level languages, of which Fortran was the main survivor.
Higher-level languages allowed more sophisticated programs to be
developed, and patterns in the use of data emerged. Whereas in Fortran data
types served primarily as cues for selecting the proper machine instructions,
Garlan & Shaw: An Introduction to Software Architecture 4
data types in Algol and it successors serve to state the programmer’s intentions
about how data should be used. The compilers for these languages could build
on experience with Fortran and tackle more sophisticated compilation
problems. Among other things, they checked adherence to these intentions,
thereby providing incentives for the programmers to use the type mechanism.
Progress in language design continued with the introduction of modules to
provide protection for related procedures and data structures, with the
separation of a module’s specification from its implementation, and with the
introduction of abstract data types.
2.2. Abstract Data Types
In the late 1960s, good programmers shared an intuition about software
development: If you get the data structures right, the effort will make
development of the rest of the program much easier. The abstract data type
work of the 1970s can be viewed as a development effort that converted this
intuition into a real theory. The conversion from an intuition to a theory
involved understanding
• the software structure (which included a representation packaged with
its primitive operators),
• specifications (mathematically expressed as abstract models or algebraic
axioms),
• language issues (modules, scope, user-defined types),
• integrity of the result (invariants of data structures and protection from
other manipulation),
• rules for combining types (declarations),
• information hiding (protection of properties not explicitly included in
specifications).
The effect of this work was to raise the design level of certain elements of
software systems, namely abstract data types, above the level of programming
language statements or individual algorithms. This form of abstraction led to
an understanding of a good organization for an entire module that serves one
particular purpose. This involved combining representations, algorithms,
specifications, and functional interfaces in uniform ways. Certain support was
required from the programming language, of course, but the abstract data type
paradigm allowed some parts of systems to be developed from a vocabulary of
data types rather than from a vocabulary of programming-language constructs.
2.3. Software Architecture
Just as good programmers recognized useful data structures in the late 1960s,
good software system designers now recognize useful system organizations.
Garlan & Shaw: An Introduction to Software Architecture 5
One of these is based on the theory of abstract data types. But this is not the
only way to organize a software system.
Many other organizations have developed informally over time, and are
now part of the vocabulary of software system designers. For example, typical
descriptions of software architectures include synopses such as (italics ours):
•“Camelot is based on the client-server model and uses remote procedure
calls both locally and remotely to provide communication among
applications and servers.”[8]
•“Abstraction layering and system decomposition provide the appearance
of system uniformity to clients, yet allow Helix to accommodate a
diversity of autonomous devices. The architecture encourages a client-
server model for the structuring of applications.”[9]
•“We have chosen a distributed, object-oriented approach to managing
information.” [10]
•“The easiest way to make the canonical sequential compiler into a
concurrent compiler is to pipeline the execution of the compiler phases
over a number of processors. . . . A more effective way [is to] split the
source code into many segments, which are concurrently processed
through the various phases of compilation [by multiple compiler
processes] before a final, merging pass recombines the object code into a
single program.”[11]
Other software architectures are carefully documented and often widely
disseminated. Examples include the International Standard Organization’s
Open Systems Interconnection Reference Model (a layered network
architecture) [12], the NIST/ECMA Reference Model (a generic software
engineering environment architecture based on layered communication
substrates) [13, 14], and the X Window System (a distributed windowed user
interface architecture based on event triggering and callbacks) [15].
We are still far from having a well-accepted taxonomy of such architectural
paradigms, let alone a fully-developed theory of software architecture. But we
can now clearly identify a number of architectural patterns, or styles, that
currently form the basic repertoire of a software architect.
3. Common Architectural Styles
We now examine some of these representative, broadly-used architectural
styles. Our purpose is to illustrate the rich space of architectural choices, and
indicate what are some of the tradeoffs in choosing one style over another.
To make sense of the differences between styles, it helps to have a common
framework from which to view them. The framework we will adopt is to treat
an architecture of a specific system as a collection of computational
Garlan & Shaw: An Introduction to Software Architecture 6
components—or simply components-—together with a description of the
interactions between these components—the connectors. Graphically speaking,
this leads to a view of an abstract architectural description as a graph in which
the nodes represent the components and the arcs represent the connectors. As
we will see, connectors can represent interactions as varied as procedure call,
event broadcast, database queries, and pipes.
An architectural style, then, defines a family of such systems in terms of a
pattern of structural organization. More specifically, an architectural style
determines the vocabulary of components and connectors that can be used in
instances of that style, together with a set of constraints on how they can be
combined. These can include topological constraints on architectural
descriptions (e.g., no cycles). Other constraints—say, having to do with
execution semantics—might also be part of the style definition.
Given this framework, we can understand what a style is by answering the
following questions: What is the structural pattern—the components,
connectors, and constraints? What is the underlying computational model?
What are the essential invariants of the style? What are some common
examples of its use? What are the advantages and disadvantages of using that
style? What are some of the common specializations?
3.1. Pipes and Filters
In a pipe and filter style each component has a set of inputs and a set of
outputs. A component reads streams of data on its inputs and produces
streams of data on its outputs, delivering a complete instance of the result in a
standard order. This is usually accomplished by applying a local
transformation to the input streams and computing incrementally so output
begins before input is consumed. Hence components are termed “filters”. The
connectors of this style serve as conduits for the streams, transmitting outputs
of one filter to inputs of another. Hence the connectors are termed “pipes”.
Among the important invariants of the style, filters must be independent
entities: in particular, they should not share state with other filters. Another
important invariant is that filters do not know the identity of their upstream
and downstream filters. Their specifications might restrict what appears on the
input pipes or make guarantees about what appears on the output pipes, but
they may not identify the components at the ends of those pipes. Furthermore,
the correctness of the output of a pipe and filter network should not depend on
the order in which the filters perform their incremental processing—although
fair scheduling can be assumed. (See [5] for an in-depth discussion of this style
and its formal properties.) Figure 1 illustrates this style.
Common specializations of this style include pipelines, which restrict the
topologies to linear sequences of filters; bounded pipes, which restrict the
amount of data that can reside on a pipe; and typed pipes, which require that
the data passed between two filters have a well-defined type.
Garlan & Shaw: An Introduction to Software Architecture 7
Data flowASCII stream
filter
C
omputation
Figure 1: Pipes and Filters
A degenerate case of a pipeline architecture occurs when each filter
processes all of its input data as a single entity.1 In this case the architecture
becomes a “batch sequential” system. In these systems pipes no longer serve
the function of providing a stream of data, and therefore are largely vestigial.
Hence such systems are best treated as instances of a separate architectural style.
The best known examples of pipe and filter architectures are programs
written in the Unix shell [16]. Unix supports this style by providing a notation
for connecting components (represented as Unix processes) and by providing
run time mechanisms for implementing pipes. As another well-known
example, traditionally compilers have been viewed as a pipeline systems
(though the phases are often not incremental). The stages in the pipeline
include lexical analysis, parsing, semantic analysis, code generation. (We
return to this example in the case studies.) Other examples of pipes and filters
occur in signal processing domains [17], functional programming [18], and
distributed systems [19].
Pipe and filter systems have a number of nice properties. First, they allow
the designer to understand the overall input/output behavior of a system as a
simple composition of the behaviors of the individual filters. Second, they
support reuse: any two filters can be hooked together, provided they agree on
the data that is being transmitted between them. Third, systems can be easily
maintained and enhanced: new filters can be added to existing systems and old
filters can be replaced by improved ones. Fourth, they permit certain kinds of
specialized analysis, such as throughput and deadlock analysis. Finally, they
naturally support concurrent execution. Each filter can be implemented as a
separate task and potentially executed in parallel with other filters.
But these systems also have their disadvantages.2 First, pipe and filter
systems often lead to a batch organization of processing. Although filters can
1In general, we find that the boundaries of styles can overlap. This should not deter us from
identifying the main features of a style with its central examples of use.
2This is true in spite of the fact that pipes and filters, like every style, has a set of devout
religious followers—people who believe that all problems worth solving can best be solved using
that particular style.
Garlan & Shaw: An Introduction to Software Architecture 8
process data incrementally, since filters are inherently independent, the
designer is forced to think of each filter as providing a complete
transformation of input data to output data. In particular, because of their
transformational character, pipe and filter systems are typically not good at
handling interactive applications. This problem is most severe when
incremental display updates are required, because the output pattern for
incremental updates is radically different from the pattern for filter output.
Second, they may be hampered by having to maintain correspondences
between two separate, but related streams. Third, depending on the
implementation, they may force a lowest com-
mon denominator on data transmission, resulting in added work for each
filter to parse and unparse its data. This, in turn, can lead both to loss of
performance and to increased complexity in writing the filters themselves.
3.2. Data Abstraction and Object-Oriented Organization
In this style data representations and their associated primitive operations are
encapsulated in an abstract data type or object. The components of this style are
the objects—or, if you will, instances of the abstract data types. Objects are
examples of a sort of component we call a manager because it is responsible for
preserving the integrity of a resource (here the representation). Objects interact
through function and procedure invocations. Two important aspects of this
style are (a) that an object is responsible for preserving the integrity of its
representation (usually by maintaining some invariant over it), and (b) that
the representation is hidden from other objects. Figure 2 illustrates this style.3
obj
obj
obj
obj
obj
obj
objobj
op
op
op
op
op
op
op
op
op
op
op
op
op
op
op
op
obj is a manager
op is an invocation
ADTManager
Proc call
Figure 2: Abstract Data Types and Objects
3We haven’t mentioned inheritance in this description. While inheritance is an important
organizing principle for defining the types of objects in a system, it does not have a direct
architectural function. In particular, in our view, an inheritance relationship is not a connector,
since it does not define the interaction between components in a system. Also, in an architectural
setting inheritance of properities is not restricted to object types—but may include connectors and
even architectural styles.
Garlan & Shaw: An Introduction to Software Architecture 9
The use of abstract data types, and increasingly the use of object-oriented
systems, is, of course, widespread. There are many variations. For example,
some systems allow “objects” to be concurrent tasks; others allow objects to
have multiple interfaces [20, 21].
Object-oriented systems have many nice properties, most of which are well
known. Because an object hides its representation from its clients, it is possible
to change the implementation without affecting those clients. Additionally,
the bundling of a set of accessing routines with the data they manipulate
allows designers to decompose problems into collections of interacting agents.
But object-oriented systems also have some disadvantages. The most
significant is that in order for one object to interact with another (via
procedure call) it must know the identity of that other object. This is in
contrast, for example, to pipe and filter systems, where filters do need not
know what other filters are in the system in order to interact with them. The
significance of this is that whenever the identity of an object changes it is
necessary to modify all other objects that explicitly invoke it. In a module-
oriented language this manifests itself as the need to change the “import” list
of every module that uses the changed module. Further there can be side-
effect problems: if A uses object B and C also uses B, then C’s effects on B look
like unexpected side effects to A, and vice versa.
3.3. Event-based, Implicit Invocation
Traditionally, in a system in which the component interfaces provide a
collection of procedures and functions, components interact with each other by
explicitly invoking those routines. However, recently there has been
considerable interest in an alternative integration technique, variously referred
to as implicit invocation, reactive integration, and selective broadcast. This
style has historical roots in systems based on actors [22], constraint satisfaction,
daemons, and packet-switched networks.
The idea behind implicit invocation is that instead of invoking a
procedure directly, a component can announce (or broadcast) one or more
events. Other components in the system can register an interest in an event by
associating a procedure with the event. When the event is announced the
system itself invokes all of the procedures that have been registered for the
event. Thus an event announcement “implicitly” causes the invocation of
procedures in other modules.
For example, in the Field system [23], tools such as editors and variable
monitors register for a debugger’s breakpoint events. When a debugger stops at
a breakpoint, it announces an event that allows the system to automatically
invoke methods in those registered tools. These methods might scroll an
editor to the appropriate source line or redisplay the value of monitored
variables. In this scheme, the debugger simply announces an event, but does
Garlan & Shaw: An Introduction to Software Architecture 10
not know what other tools (if any) are concerned with that event, or what they
will do when that event is announced.
Architecturally speaking, the components in an implicit invocation style
are modules whose interfaces provide both a collection of procedures (as with
abstract data types) and a set of events. Procedures may be called in the usual
way. But in addition, a component can register some of its procedures with
events of the system. This will cause these procedures to be invoked when
those events are announced at run time. Thus the connectors in an implicit
invocation system include traditional procedure call as well as bindings
between event announcements and procedure calls.
The main invariant of this style is that announcers of events do not know
which components will be affected by those events. Thus components cannot
make assumptions about order of processing, or even about what processing,
will occur as a result of their events. For this reason, most implicit invocation
systems also include explicit invocation (i.e., normal procedure call) as a
complementary form of interaction.
Examples of systems with implicit invocation mechanisms abound [7].
They are used in programming environments to integrate tools [23, 24], in
database management systems to ensure consistency constraints [22, 25], in user
interfaces to separate presentation of data from applications that manage the
data [26, 27], and by syntax-directed editors to support incremental semantic
checking [28, 29].
One important benefit of implicit invocation is that it provides strong
support for reuse. Any component can be introduced into a system simply by
registering it for the events of that system. A second benefit is that implicit
invocation eases system evolution [30]. Components may be replaced by other
components without affecting the interfaces of other components in the
system.
In contrast, in a system based on explicit invocation, whenever the identity
of a that provides some system function is changed, all other modules that
import that module must also be changed.
The primary disadvantage of implicit invocation is that components
relinquish control over the computation performed by the system. When a
component announces an event, it has no idea what other components will
respond to it. Worse, even if it does know what other components are
interested in the events it announces, it cannot rely on the order in which they
are invoked. Nor can it know when they are finished. Another problem
concerns exchange of data. Sometimes data can be passed with the event. But
in other situations event systems must rely on a shared repository for
interaction. In these cases global performance and resource management can
become a serious issue. Finally, reasoning about correctness can be
problematic, since the meaning of a procedure that announces events will
Garlan & Shaw: An Introduction to Software Architecture 11
depend on the context of bindings in which it is invoked. This is in contrast to
traditional reasoning about procedure calls, which need only consider a
procedure’s pre- and post-conditions when reasoning about an invocation of it.
3.4. Layered Systems
A layered system is organized hierarchically, each layer providing service to
the layer above it and serving as a client to the layer below. In some layered
systems inner layers are hidden from all except the adjacent outer layer, except
for certain functions carefully selected for export. Thus in these systems the
components implement a virtual machine at some layer in the hierarchy. (In
other layered systems the layers may be only partially opaque.) The connectors
are defined by the protocols that determine how the layers will interact.
Topological constraints include limiting interactions to adjacent layers. Figure
3 illustrates this style.
Core
Level
Basic Utility
Useful Systems
Users
omposites of
arious elements
sually
rocecure calls
Figure 3: Layered Systems
The most widely known examples of this kind of architectural style are
layered communication protocols [31]. In this application area each layer
provides a substrate for communication at some level of abstraction. Lower
levels define lower levels of interaction, the lowest typically being defined by
hardware connections. Other appli-cation areas for this style include database
systems and operating systems [9, 32, 33 ].
Layered systems have several desirable properties. First, they support
design based on increasing levels of abstraction. This allows implementors to
partition a complex problem into a sequence of incremental steps. Second,
they support enhancement. Like pipelines, because each layer interacts with at
most the layers below and above, changes to the function of one layer affect at
most two other layers. Third, they support reuse. Like abstract data types,
different implementations of the same layer can be used interchangeably,
provided they support the same interfaces to their adjacent layers. This leads
to the possibility of defining standard layer interfaces to which different
Garlan & Shaw: An Introduction to Software Architecture 12
implementors can build. (A good example is the OSI ISO model and some of
the X Window System protocols.)
But layered systems also have disadvantages. Not all systems are easily
structured in a layered fashion. (We will see an example of this later in the
case studies.) And even if a system can logically be structured as layers,
considerations of performance may require closer coupling between logically
high-level functions and their lower-level implementations. Additionally, it
can be quite difficult to find the right levels of abstraction. This is particularly
true for standardized layered models. One notes that the communications
community has had some difficulty mapping existing protocols into the ISO
framework: many of those protocols bridge several layers.
In one sense this is similar to the benefits of implementation hiding found
in abstract data types. However, here there are multiple levels of abstraction
and implementation. They are also similar to pipelines, in that components
communicate at most with one other component on either side. But instead of
simple pipe read/write protocol of pipes, layered systems can provide much
richer forms of interaction. This makes it difficult to define system-
independent layers (as with filters)—since a layer must support the specific
protocols at its upper and lower boundaries. But it also allows much closer
interaction between layers, and permits two-way transmission of information.
3.5. Repositories
In a repository style there are two quite distinct kinds of components: a central
data structure represents the current state, and a collection of independent
components operate on the central data store. Interactions between the
repository and its external components can vary significantly between systems.
The choice of control discipline leads to major subcategories. If the types of
transactions in an input stream of transactions trigger selection of processes to
execute, the repository can be a traditional database. If the current state of the
central data structure is the main trigger of selecting processes to execute, the
repository can be a blackboard.
Figure 4 illustrates a simple view of a blackboard architecture. (We will
examine more detailed models in the case studies.) The blackboard model is
usually presented with three major parts:
The knowledge sources: separate, independent parcels of application-
dependent knowledge. Interaction among knowledge sources takes
place solely through the blackboard.
The blackboard data structure: problem-solving state data, organized into an
application-dependent hierarchy. Knowledge sources make changes to
the blackboard that lead incrementally to a solution to the problem.
Garlan & Shaw: An Introduction to Software Architecture 13
Control: driven entirely by state of blackboard. Knowledge sources respond
opportunistically when changes in the blackboard make them applicable.
Blackboard
(shared
data)
ks1 ks2
ks3
ks4
ks5ks6
ks7
ks8
omputation
M
emory
irect access
Figure 4: The Blackboard
In the diagram there is no explicit representation of the control
component. Invocation of a knowledge source is triggered by the state of the
blackboard. The actual locus of control, and hence its implementation, can be
in the knowledge sources, the blackboard, a separate module, or some
combination of these.
Blackboard systems have traditionally been used for applications requiring
complex interpretations of signal processing, such as speech and pattern
recognition. Several of these are surveyed by Nii [34]. They have also appeared
in other kinds of systems that involve shared access to data with loosely
coupled agents [35].
There are, of course, many other examples of repository systems. Batch-
sequential systems with global databases are a special case. Programming
environments are often organized as a collection of tools together with a
shared repository of programs and program fragments [36]. Even applications
that have been traditionally viewed as pipeline architectures, may be more
accurately interpreted as repository systems. For example, as we will see later,
while a compiler architecture has traditionally been presented as a pipeline, the
“phases” of most modern compilers operate on a base of shared information
(symbol tables, abstract syntax tree, etc.).
3.6. Table Driven Interpreters
In an interpreter organization a virtual machine is produced in software. An
interpreter includes the pseudo-program being interpreted and the
interpretation engine itself. The pseudo-program includes the program itself
and the interpreter’s analog of its execution state (activation record). The
interpretation engine includes both the definition of the interpreter and the
current state of its execution. Thus an interpreter generally has four
components: an interpretation engine to do the work, a memory that contains
Garlan & Shaw: An Introduction to Software Architecture 14
the pseudo-code to be interpreted, a representation of the control state of the
interpretation engine, and a representation of the current state of the program
being simulated. (See Figure 5.)
Data
(program
state)
Inputs
Outputs
Selected data
Selected instructionSimulated
Interp-
retation
Engine
Internal
I
nterpreter
State
Program
Being
Interpreted
Memory
Computation
state mach
Data access
Fetch/store
Figure 5:
Interpreter
Interpreters are commonly used to build virtual machines that close the
gap between the computing engine expected by the semantics of the program
and the computing engine available in hardware. We occasionally speak of a
programming language as providing, say, a “virtual Pascal machine.”
We will return to interpreters in more detail in the case studies.
3.7. Other Familiar Architectures
There are numerous other architectural styles and patterns. Some are
widespread and others are specific to particular domains. While a complete
treatment of these is beyond the scope of this paper, we briefly note a few of the
important categories.
• Distributed processes: Distributed systems have developed a number of
common organizations for multi-process systems [37]. Some can be
characterized primarily by their topological features, such as ring and
star organizations. Others are better characterized in terms of the kinds
of inter-process protocols that are used for communication (e.g.,
heartbeat algorithms).
One common form of distributed system architecture is a “client-server”
organization [38]. In these systems a server represents a process that provides
services to other processes (the clients). Usually the server does not know in
advance the identities or number of clients that will access it at run time. On
the other hand, clients know the identity of a server (or can find it out through
some other server) and access it by remote procedure call.
• Main program/subroutine organizations: The primary organization of
many systems mirrors the programming language in which the system
Garlan & Shaw: An Introduction to Software Architecture 15
is written. For languages without support for modularization this often
results in a system organized around a main program and a set of
subroutines. The main program acts as the driver for the subroutines,
typically providing a control loop for sequencing through the
subroutines in some order.
• Domain-specific software architectures: Recently there has been
considerable interest in developing “reference” architectures for specific
domains [39]. These architectures provide an organizational structure
tailored to a family of applications, such as avionics, command and
control, or vehicle management systems. By specializing the
architecture to the domain, it is possible to increase the descriptive
power of structures. Indeed, in many cases the architecture is sufficiently
constrained that an executable system can be generated automatically or
semi-automatically from the architectural description itself.
• State transition systems: A common organization for many reactive
systems is the state transition system [40]. These systems are defined in
terms a set of states and a set of named transitions that move a system
from one state to another.
• Process control systems: Systems intended to provide dynamic control
of a physical environment are often organized as process control systems
[41]. These systems are roughly characterized as a feedback loop in which
inputs from sensors are used by the process control system to determine
a set of outputs that will produce a new state of the environment.
3.8. Heterogeneous Architectures
Thus far we have been speaking primarily of “pure” architectural styles.
While it is important to understand the individual nature of each of these
styles, most systems typically involve some combination of several styles.
There are different ways in which architectural styles can be combined.
One way is through hierarchy. A component of a system organized in one
architectural style may have an internal structure that is developed a
completely different style. For example, in a Unix pipeline the individual
components may be represented internally using virtually any style—
including, of course, another pipe and filter, system.
What is perhaps more surprising is that connectors, too, can often be
hierarchically decomposed. For example, a pipe connector may be
implemented internally as a FIFO queue accessed by insert and remove
operations.
A second way for styles to be combined is to permit a single component to
use a mixture of architectural connectors. For example, a component might
access a repository through part of its interface, but interact through pipes with
other components in a system, and accept control information through
Garlan & Shaw: An Introduction to Software Architecture 16
another part of its interface. (In fact, Unix pipe and filter systems do this, the
file system playing the role of the repository and initialization switches playing
the role of control.)
Another example is an “active database”. This is a repository which
activates external components through implicit invocation. In this
organization external components register interest in portions of the database.
The database automatically invokes the appropriate tools based on this
association. (Blackboards are often constructed this way; knowledge sources are
associated with specific kinds of data, and are activated whenever that kind of
data is modified.)
A third way for styles to be combined is to completely elaborate one level of
architectural description in a completely different architectural style. We will
see examples of this in the case studies.
4. Case Studies
We now present six examples to illustrate how architectural principles can be
used to increase our understanding of software systems. The first example
shows how different architectural solutions to the same problem provide
different benefits. The second case study summarizes experience in developing
a a domain-specific architectural style for a family of industrial products. The
third case study examines the familiar compiler architecture in a fresh light.
The remaining three case studies present examples of the use of heterogeneous
architectures.
4.1. Case Study 1: Key Word in Context
In his paper of 1972, Parnas proposed the following problem [42]:
The KWIC [Key Word in Context] index system accepts an ordered set of
lines, each line is an ordered set of words, and each word is an ordered
set of characters. Any line may be “circularly shifted” by repeatedly
removing the first word and appending it at the end of the line. The
KWIC index system outputs a listing of all circular shifts of all lines in
alphabetical order.
Parnas used the problem to contrast different criteria for decomposing a
system into modules. He describes two solutions, one based on functional
decomposition with shared access to data representations, and a second based
on a decomposition that hides design decisions. Since its introduction, the
problem has become well-known and is widely used as a teaching device in
software engineering. Garlan, Kaiser, and Notkin also use the problem to
illustrate modularization schemes based on implicit invocation [7].
While KWIC can be implemented as a relatively small system it is not
simply of pedagogical interest. Practical instances of it are widely used by
Garlan & Shaw: An Introduction to Software Architecture 17
computer scientists. For example, the “permuted” [sic] index for the Unix Man
pages is essentially such a system.
From the point of view of software architecture, the problem derives its
appeal from the fact that it can be used to illustrate the effect of changes on
software design. Parnas shows that different problem decompositions vary
greatly in their ability to withstand design changes. Among the changes he
considers are:
• Changes in processing algorithm: For example, line shifting can be
performed on each line as it is read from the input device, on all the
lines after they are read, or on demand when the alphabetization
requires a new set of shifted lines.
• Changes in data representation: For example, lines can be stored in
various ways. Similarly, circular shifts can be stored explicitly or
implicitly (as pairs of index and offset).
Garlan, Kaiser, and Notkin, extend Parnas’ analysis by considering:
• Enhancement to system function: For example, modify the system so
that shifted lines to eliminate circular shifts that start with certain noise
words (such as “a”, “an”, “and”, etc.). Change the system to be
interactive, and allow the user to delete lines from the original (or,
alternatively, from circularly shifted) lists.
• Performance: Both space and time.
• Reuse: To what extent can the components serve as reusable entities.
We now outline four architectural designs for the KWIC system. All four
are grounded in published solutions (including implementations). The first
two are those considered in Parnas’ original article. The third solution is based
on the use of an implicit invocation style and represents a variant on the
solution examined by Garlan, Kaiser, and Notkin. The fourth is a pipeline
solution inspired by the Unix index utility.
After presenting each solution and briefly summarizing its strengths and
weakness, we contrast the different architectural decompositions in a table
organized along the five design dimensions itemized above.
Solution 1: Main Program/Subroutine with Shared Data
The first solution decomposes the problem according to the four basic
functions performed: input, shift, alphabetize, and output. These
computational components are coordinated as subroutines by a main program
that sequences through them in turn. Data is communicated between the
components through shared storage (“core storage”). Communication between
the computational components and the shared data is an unconstrained read-
Garlan & Shaw: An Introduction to Software Architecture 18
write protocol. This is made possible by the fact that the coordinating program
guarantees sequential access to the data. (See Figure 6.)
Master Control
Input Circular Shift Alphabetizer
Output
Input
Medium
Characters Index Alphabetized
Index
Output
Medium
Direct Memory Access
System I/O
Subprogram Call
Figure 6: KWIC – Shared Data Solution
Using this solution data can be represented efficiently, since computations
can share the same storage. The solution also has a certain intuitive appeal,
since distinct computational aspects are isolated in different modules.
However, as Parnas argues, it has a number of serious drawbacks in terms
of its ability to handle changes. In particular, a change in data storage format
will affect almost all of the modules. Similarly changes in the overall
processing algorithm and enhancements to system function are not easily
accomodated. Finally, this decom-position is not particularly supportive of
reuse.
Solution 2: Abstract Data Types
The second solution decomposes the system into a similar set of five modules.
However, in this case data is no longer directly shared by the computational
components. Instead, each module provides an interface that permits other
components to access data only by invoking procedures in that interface. (See
Figure 7, which illustrates how each of the components now has a set of
procedures that determine the form of access by other components in the
system.)
This solution provides the same logical decomposition into processing
modules as the first. However, it has a number of advantages over the first
solution when design changes are considered. In particular, both algorithms
and data representations can be changed in individual modules without
affecting others. Moreover, reuse is better supported than in the first solution
Garlan & Shaw: An Introduction to Software Architecture 19
because modules make fewer assumptions about the others with which they
interact.
Master Control
Input Output
Input
Medium
Output
Medium
System I/O
Subprogram Call
Characters Circular Shift
Alphabetic
Shifts
Figure 7: KWIC – Abstract Data Type Solution
On the other hand, as discussed by Garlan, Kaiser, and Notkin, the solution
is not particularly well-suited to enhancements. The main problem is that to
add new functions to the system, the implementor must either modify the
existing modules—compromising their simplicity and integrity—or add new
modules that lead to performance penalties. (See [7] for a detailed discussion.)
Solution 3: Implicit Invocation
The third solution uses a form of component integration based on shared data
similar to the first solution. However, there are two important differences.
First, the interface to the data is more abstract. Rather than exposing the
storage formats to the computing modules, data is accessed abstractly (for
example, as a list or a set). Second, computations are invoked implicitly as data
is modified. Thus interaction is based on an active data model. For example,
the act of adding a new line to the line storage causes an event to be sent to the
shift module. This allows it to produce circular shifts (in a separate abstract
shared data store). This in turn causes the alphabetizer to be implicitly
invoked so that it can alphabetize the lines.
This solution easily supports functional enhancements to the system:
additional modules can be attached to the system by registering them to be
invoked on data-changing events. Because data is accessed abstractly, it also
insulates computations from changes in data representation. Reuse is also
supported, since the implicitly invoked modules only rely on the existence of
certain externally triggered events.
Garlan & Shaw: An Introduction to Software Architecture 20
Output
Medium
Master Control
Input Output
Input
Medium
Lines
Circular
Shift
Alphabetizer
Lines
System I/O
Subprogram Call
Implicit Invocation
Figure 8: KWIC – Implicit Invocation Solution
However, the solution suffers from the fact that it can be difficult to control
the order of processing of the implicitly invoked modules. Further, because
invocations are data driven, the most natural implementations of this kind of
decomposition tend to use more space than the previously considered
decompositions.
Solution 4: Pipes and Filters
The fourth solution uses a pipeline solution. In this case there are four filters:
input, shift, alphabetize, and output. Each filter processes the data and sends it
to the next filter. Control is distributed: each filter can run whenever it has
data on which to compute. Data sharing between filters is strictly limited to
that transmitted on pipes. (See Figure 9.)
This solution has several nice properties. First, it maintains the intuitive
flow of processing. Second, it supports reuse, since each filter can function in
isolation (provided upstream filters produce data in the form it expects). New
functions are easily added to the system by inserting filters at the appropriate
point in the processing sequence. Third, it supports ease of modification, since
filters are logically independent of other filters.
Input
Output
Input
Medium
Output
Medium
Circular
Shift
Alphabetizer
System I/O
Pipe
Figure 9: KWIC – Pipe and Filter Solution
Garlan & Shaw: An Introduction to Software Architecture 21
On the other hand it has a number of drawbacks. First, it is virtually
impossible to modify the design to support an interactive system. For example,
in order to delete a line, there would have to be some persistent shared storage,
violating a basic tenet of this approach. Second, the solution is inefficient in
terms of its use of space, since each filter must copy all of the data to its output
ports.
C o m p a r i s o n s
The solutions can be compared by tabulating their ability to address the design
considerations itemized earlier. A detailed comparison would have to involve
consideration of a number of factors concerning the intended use of the
system: for example, is it batch or interactive, update-intensive or query-
intensive, etc.
Figure 10 provides an approximation to such an analysis, based on the
discussion of architectural styles introduced earlier. As Parnas pointed out, the
shared data solution is particularly weak in its support for changes in the
overall processing algorithm, data representations, and reuse. On the other
hand it can achieve relatively good performance, by virtue of direct sharing of
data. Further, it is relatively easy to add a new processing component (also
accessing the shared data). The abstract data type solution allows changes to
data representation and supports reuse, without necessarily compromising
performance. However, the interactions between components in that solution
are wired into the modules themselves, so changing the overall processing
algorithm or adding new functions may involve a large number of changes to
the existing system.
_
_
+
+
_
Shared
Memory ADT Events Dataflow
_
+
_
+
+
+
_
+
_
_
+
_
+
_
+
Change in Algorithm
Change in Data Repn
Change in Function
Performance
Reuse
Figure 10: KWIC – Comparison of Solutions
The implicit invocation solution is particularly good for adding new
functionality. However, it suffers from some of the problems of the shared
data approach: poor support for change in data representation and reuse.
Moreover, it may introduce extra execution overhead. The pipe and filter
solution allows new filters to be placed in the stream of text processing.
Therefore it supports changes in processing algorithm, changes in function,
Garlan & Shaw: An Introduction to Software Architecture 22
and reuse. On the other hand, decisions about data representation will be
wired into the assumptions about the kind of data that is transmitted along the
pipes. Further, depending on the exchange format, there may be additional
overhead involved in parsing and unparsing the data onto pipes.
4.2. Case Study 2: Instrumentation
Software
Our second case study describes the industrial development of a software
architecture at Tektronix, Inc. This work was carried out as a collaborative
effort between several Tektronix product divisions and the Computer
Research Laboratory over a three year period [6].
The purpose of the project was to develop a reusable system architecture
for oscilloscopes. An oscilloscope is an instrumentation system that samples
electrical signals and displays pictures (called traces) of them on a screen.
Additionally, oscilloscopes perform measurements on the signals, and also
display these on the screen. While oscilloscopes were once simple analogue
devices involving little software, modern oscilloscopes rely primarily on
digital technology and have quite complex software. It is not uncommon for a
modern oscilloscope to perform dozens of measurements, supply megabytes of
internal storage, interface to a network of workstations and other instruments,
and provide sophisticated user interface including a touch panel screen with
menus, built-in help facilities, and color displays.
Like many companies that have had to rely increasingly on software to
support their products, Tektronix was faced with number of problems. First,
there was little reuse across different oscilloscope products. Instead, different
oscilloscopes were built by different product divisions, each with their own
development conventions, software organization, programming language, and
development tools. Moreover, even within a single product division, each
new oscilloscope typically required a redesign from scratch to accommodate
changes in hardware capability and new requirements on the user interface.
This problem was compounded by the fact that both hardware and interface
requirements were changing increasingly rapidly. Furthermore, there was a
perceived need to address “specialized markets”. To do this it would have to
be possible to tailor a general-purpose instrument, to a specific set of uses.
Second, there were increasing performance problems because the software
was not rapidly configurable within the instrument. This problem arises
because an oscilloscope can be configured in many different modes, depending
on the user’s task. In old oscilloscopes reconfiguration was handled simply by
loading different software to handle the new mode. But as the total size of
software was increasing, this was leading to delays between a user’s request for
a new mode and a reconfigured instrument.
The goal of the project was to develop an architectural framework for
oscilloscopes that would address these problems. The result of that work was a
domain-specific software architecture that formed the basis of the next
Garlan & Shaw: An Introduction to Software Architecture 23
generation of Tektronix oscilloscopes. Since then the framework has been
extended and adapted to accommodate a broader class of system, while at the
same time being better adapted to the specific needs of instrumentation
software.
In the remainder of this section, we outline the stages in this architectural
development.
An object-oriented model
The first attempt at developing a reusable architecture focused on producing an
object-oriented model of the software domain. This led to a clarification of the
data types used in oscilloscopes: waveforms, signals, measurements, trigger
modes, etc. (See Figure 11.)
waveform
x-y wvfm accumulate wvfmmax-min wvfm
Oscilloscope
object
•••
Figure 11: Oscilloscopes – An Object-oriented Model
While this was a useful exercise, it fell far short of producing the hoped-for
results. Although many types of data were identified, there was no overall
model that explained how the types fit together. This led to confusion about
the partitioning of functionality. For example, should measurements be
associated with the types of data being measured, or represented externally?
Which objects should the user interface talk to?
A layered model
The second phase attempted to correct these problems by providing a layered
model of an oscilloscope. (See Figure 11.) In this model the core layer
represented the signal manipulation functions that filter signals as they enter
the oscilloscope. These functions are typically implemented in hardware. The
next layer represented waveform acquisition. Within this layer signals are
digitized and stored internally for later processing. The third layer consisted of
waveform manipulation, including measurement, waveform addition,
Fourier transformation, etc. The fourth layer consisted of display functions.
This layer was responsible for mapping digitized waveforms and
measurements to visual representations. The outermost layer was the user
interface. This layer was responsible for interacting with the user and for
deciding which data should be shown on the screen. (See Figure 12.)
Garlan & Shaw: An Introduction to Software Architecture 24
Hardware
Digitization
Visualization
User interface
Figure 12: Oscilloscopes – A Layered Model
This layered model was intuitively appealing since it partitioned the
functions of an oscilloscope into well-defined groupings. Unfortunately it was
the wrong model for the application domain. The main problem was that the
boundaries of abstraction enforced by the layers conflicted with the needs for
interaction between the various functions. For example, the model suggests
that all user interactions with an oscilloscope should be in terms of the visual
representations. But in practice real oscilloscope users need to directly affect
the functions in all layers, such as setting attenuation in the signal
manipulation layer, choosing acquisition mode and parameters in the
acquisition layer, or creating derived waveforms in the waveform
manipulation layer.
A Pipe and Filter Model
The third attempt yielded a model in which oscilloscope functions were
viewed as incremental transformers of data. Signal transformers serve to
condition external signals. Acquisition transformers derive digitized
waveforms from these signals. Display transformers convert these waveforms
into visual data. (See Figure 13.)
Trigger subsystem
Couple Acquire To-XY Clip
Measure
Signal
Waveform Trace
Measurement
Times
Figure 13: Oscilloscopes – A Pipe and Filter Model
This architectural model was a significant improvement over the layered
model in that it did not isolate the functions in separate partitions. For
example, nothing in this model would prevent signal data directly feeding into
display filters. Further, the model corresponded well to the engineers’ view of
signal processing as a dataflow problem. The main problem with the model
was that it was not clear how the user should interact with it. If the user were
simply at one end of the system, then this would represent an even worse
decomposition than the layered system.
Garlan & Shaw: An Introduction to Software Architecture 25
A Modified Pipe and Filter Model
The fourth solution accounted for user inputs by associating with each filter a
control interface that allows an external entity to set parameters of operation
for the filter. For example, the acquisition filter might have parameters that
determine sample rate and waveform duration. These inputs serve as
configuration parameters for the oscilloscope. Formally, the filters can be
modelled as “higher-order” functions, for which the configuration parameters
determine what data transformation the filter will perform. (See [17] for this
interpretation of the architecture.) Figure 14 illustrates this architecture.
Couple Acquire ClipTo-XY
Measure
Signal
Waveform Trace
Measurement
Times
Coupling Kind,Rate Trans Size
Trigger subsystem
Figure 14: Oscilloscopes – A Modified Pipe and Filter Model
The introduction of a control interface solves a large part of the user
interface problem. First, it provides a collection of settings that determine
what aspects of the oscilloscope can be modified dynamically by the user. It
also explains how changes to oscilloscope function can be accomplished by
incremental adjustments to the software. Second it decouples the signal
processing functions of the oscilloscope from the actual user interface: the
signal processing software makes no assumptions about how the user actually
communicates changes to its control parameters. Conversely, the actual user
interface can treat the signal processing functions solely in terms of the control
parameters. This allowed the designers to change the implementation of the
signal processing software and hardware without impacting an interface,
provided the control interface remained unchanged.
Further Specialization
The adapted pipe and filter model was a great improvement. But it, too, had
some problems. The most significant problem was that the pipe and filter
computational model led to poor performance. In particular, waveforms can
occupy a large amount of internal storage: it is simply not practical for each
filter to copy waveforms every time they process them. Further, different
filters may run at radically different speeds: it is unacceptable to slow one filter
down because another filter is still processing its data.
To handle these problems the model was further specialized. Instead of
having a single kind of pipe, several “colors” of pipes were introduced. Some
of these allowed data to be processed without copying. Others permitted data to
be ignored by slow filters if they were in the middle of processing other data.
These additional pipes increased the stylistic vocabulary and allowed the
Garlan & Shaw: An Introduction to Software Architecture 26
pipe/filter computations to be tailored more directly to the performance needs
of the product.
S u m m a r y
This case study illustrates the issues involved in developing an architectural
style for a real application domain. It underscores the fact that different
architectural styles have different effects on the ability to solve a set of
problems. Moreover, it illustrates that architectural designs for industrial
software must typically be adapted from pure forms to specialized styles that
meet the needs of the specific domain. In this case, the final result depended
greatly on the properties of pipe and filter architectures, but found ways to
adapt that generic style so that it could also satisfy the performance needs of the
product family.
4.3. Case 3: A Fresh View of Compilers
The architecture of a system can change in response to improvements in
technology. This can be seen in the way we think about compilers.
In the 1970s, compilation was regarded as a sequential process, and the
organization of a compiler was typically drawn as in Figure 15. Text enters at
the left end and is transformed in a variety of ways—to lexical token stream,
parse tree, intermediate code—before emerging as machine code on the right.
We often refer to this compilation model as a pipeline, even though it was (at
least originally) closer to a batch sequential architecture in which each
transformation (“pass”) was completed before the next one started.
Lex Syn Sem Opt
Code
Text Code
Figure 15: Traditional Compiler Model
In fact, even the batch sequential version of this model was not completely
accurate. Most compilers created a separate symbol table during lexical analysis
and used or updated it during subsequent passes. It was not part of the data
that flowed from one pass to another but rather existed outside all the passes.
So the system structure was more properly drawn as in Figure 16.
SymTab
Lex Syn Sem Opt Code
Text Code
Figure 16: Traditional Compiler Model with Shared Symbol Table
As time passed, compiler technology grew more sophisticated. The
algorithms and representations of compilation grew more complex, and
increasing attention turned to the intermediate representation of the program
Garlan & Shaw: An Introduction to Software Architecture 27
during compilation. Improved theoretical understanding, such as attribute
grammers, accelerated this trend. The consequence was that by the mid-1980s
the intermediate representation (for example, an attributed parse tree), was the
center of attention. It was created early during compilation and manipulated
during the remainder; the data structure might change in detail, but it
remained substantially one growing structure throughout. However, we
continued (sometimes to the present) to model the compiler with sequential
data flow as in Figure 17.
Lex Syn Sem Opt Code
SymTab
Tree
Text Code
Computations
(transducers and
transforms)
Memory
Data fetch/store
Vestigal data flow
Figure 17: Modern Canonical Compiler
In fact, a more appropriate view of this structure would re-direct attention
from the sequence of passes to the central shared representation. When you
declare that the tree is the locus of compilation information and the passes
define operations on the tree, it becomes natural to re-draw the architecture as
in Figure 18. Now the connections between passes denote control flow, which
is a more accurate depiction; the rather stronger connections between the
passes and the tree/symbol table structure denote data access and
manipulation. In this fashion, the architecture has become a repository, and
that is indeed a more appropriate way to think about a compiler of this class.
Happily, this new view also accommodates various tools that operate on
the internal representation rather than the textual form of a program; these
include syntax-directed editors and various analysis tools.
SymTab
TreeLex
Syn
Sem
Opt2
Code
Edit Syn
Might be
rule-basedOpt1
Figure 18: Canonical Compiler, Revisited
Garlan & Shaw: An Introduction to Software Architecture 28
Note that this repository resembles a blackboard in some respects and
differs in others. Like a blackboard, the information of the computation is
located centrally and operated on by independent computations which interact
only through the shared data. However, whereas the execution order of the
operations in a blackboard is determined by the types of the incoming database
modifications, the execution order of the compiler is predetermined.
4.4. Case 4: A Layered Design with Different Styles for the Layers
The PROVOX® system by Fisher Controls offers distributed process control for
chemical production processes [43]. Process control capabilities range from
simple control loops that control pressure, flow, or levels to complex strategies
involving interrelated control loops. Provisions are made for integration with
plant management and information systems in support of computer integrated
manufacturing. The system architecture integrates process control with plant
management and information systems in a 5-level layered hierarchy. Figure
19 shows this hierarchy: the right side is the software view, and the left side is
the hardware view.
PROVOXplus Controllers
PROVUE Consoles
ENVOX Configuration
PROVOXplus
Application Software
CorpComputers
Interface to
Host Computers
Pro
ces
s M
eas
ure
me
nt
and
Co
ntr
ol
Pro
ces
s
Su
pe
rvis
ion
Pro
ces
s
Ma
na
ge
me
nt
Co
rp
Mg
mt
.
Pla
nt
Ma
na
ge
me
nt
Level 5
Level 4
Level 3
Level 2
Level 1
Software
Figure 19: PROVOX – Hierarchical Top Level
Each level corresponds to a different process management function with its
own decision-support requirements.
• Level 1: Process measurement and control: direct adjustment of final
control elements.
• Level 2: Process supervision: operations console for monitoring and
controlling Level 1.
Garlan & Shaw: An Introduction to Software Architecture 29
• Level 3: Process management: computer-based plant automation,
including management reports, optimization strategies, and guidance to
operations console.
• Levels 4 and 5: Plant and corporate management: higher-level
functions such as cost accounting, inventory control, and order
processing/scheduling.
Different kinds of computation and response times are required at different
levels of this hierarchy. Accordingly, different computational models are used.
Levels 1 to 3 are object-oriented; Levels 4 and 5 are largely based on
conventional data-processing repository models. For present purposes it
suffices to examine the object-oriented model of Level 2 and the repositories of
Levels 4 and 5.
T
AG
DATA ACTION ERVICES
O
PERATING
T
UNING
C
ONFIGURATION
LGORITHM
AME(S)
EMPLATE
AG
ATA CTION SERVICES
OPERATING
TUNING
CONFIGURATION
ALGORITHM
NAME(S)
TEMPLATE
TAG
DATA ACTION SERVICES
OPERATING
TUNING
CONFIGURATION
ALGORITHM
NAME(S)
TEMPLATE
TAG
DATA ACTION SERVICES
PERATING
UNING
ONFIGURATION
COMMUNICATION
TRACE
ALARMS
ETC
ALGORITHM
NAME(S)
TEMPLATE
Figure 20: PROVOX – Object-oriented Elaboration
For the control and monitoring functions of Level 2, PROVOX uses a set of
points, or loci of process control. Figure 20 shows the canonical form of a point
definition; seven specialized forms support the most common kinds of
control. Points are, in essence, object-oriented design elements that
encapsulate information about control points of the process. The points are
individually configured to achieve the desired control strategy. Data associated
with a point includes: Operating parameters, including current process value,
setpoint (target value), valve output, and mode (automatic or manual).
Tuning parameters, such as gain, reset, derivative, and alarm trip-points.
Configuration parameters, including tag (name) and I/O channels.
In addition, the point’s data can include a template for a control strategy.
Like any good object, a point also includes procedural definitions such as
control algorithms, communication connections, reporting services, and trace
facilities. A collection of points implements the desired process control
strategy through the communication services and through the actual dynamics
Garlan & Shaw: An Introduction to Software Architecture 30
of the process (e.g., if one point increases flow into a tank, the current value of
a point that senses tank level will reflect this change). Although the
communication through process state deviates from the usual procedural or
message-based control of objects, points are conceptually very like objects in
their encapsulation of essential state and action information.
Reports from points appear as input transactions to data collection and
analysis processes at higher design levels. The organization of the points into
control processes can be defined by the designer to match the process control
strategy. These can be further aggregated into Plant Process Areas (points
related to a set of equipment such as a cooling tower) and thence into Plant
Management Areas (segments of a plant that would be controlled by single
operators).
PROVOX makes provisions for integration with plant management and
business systems at Levels 4 and 5. Selection of those systems is often
independent of process control design; PROVOX does not itself provide MIS
systems directly but does provide for integrating a conventional host computer
with conventional database management. The data collection facilities of
Level 3, the reporting facilities of Level 2, and the network that supports
distributed implementation suffice to provide process information as
transactions to these databases. Such databases are commonly designed as
repositories, with transaction processing functions supporting a central data
store—quite a different style from the object-oriented design of Level 2.
The use of hierarchical layers at the top level of a system is fairly common.
This permits strong separation of different classes of function and clean
interfaces between the layers. However, within each layer the interactions
among components are often too intricate to permit strict layering.
4.5. Case 5: An Interpreter Using Different Idioms for the Components
Rule-based systems provide a means of codifying the problem-solving know-
how of human experts. These experts tend to capture problem-solving
techniques as sets of situation-action rules whose execution or activation is
sequenced in response to the conditions of the computation rather than by a
predetermined scheme. Since these rules are not directly executable by
available computers, systems for interpreting such rules must be provided.
Hayes-Roth surveyed the architecture and operation of rule-based systems [44].
The basic features of a rule-based system, shown in Hayes-Roth’s rendering
as Figure 21, are essentially the features of a table-driven interpreter, as
outlined earlier.
• The pseudo-code to be executed, in this case the knowledge base
• The interpretation engine, in this case the rule interpreter, the heart of
the inference engine
Garlan & Shaw: An Introduction to Software Architecture 31
• The control state of the interpretation engine, in this case the rule and
data element selector
• The current state of the program running on the virtual machine, in
this case the working memory.
Rule
Base
Fact
Memory
Knowledge Base
Working
Memory
Inputs
Outputs
Selected data
Selected ruleRule
Interpreter
Rule and
Data Element
Selection
emory
omputation
tate mach
Figure 21: Basic Rule-Based System
Rule-based systems make heavy use of pattern matching and context
(currently relevant rules). Adding special mechanisms for these facilities to
the design leads to the more complicated view shown in Figure 22. In adding
this complexity, the original simple interpreter vanishes in a sea of new
interactions and data flows. Although the interfaces among the original
modules remain, they are not distinguished from the newly-added interfaces.
However, the interpreter model can be rediscovered by identifying the
components of Figure 22 with their design antecedents in Figure 21. This is
done in Figure 23. Viewed in this way, the elaboration of the design becomes
much easier to explain and understand. For example, we see that:
• The knowledge base remains a relatively simple memory structure,
merely gaining substructure to distinguish active from inactive
contents.
• The rule interpreter is expanded with the interpreter idiom (that is, the
interpretation engine of the rule-based system is itself implemented as a
table-driven interpreter), with control procedures playing the role of the
pseudo-code to be executed and the execution stack the role of the
current program state.
• “Rule and data element selection” is implemented primarily as a
pipeline that progressively transforms active rules and facts to
prioritized activations; in this pipeline the third filter (“nominators”)
also uses a fixed database of metarules.
• Working memory is not further elaborated.
Garlan & Shaw: An Introduction to Software Architecture 32
Inactive
rules
Active
rules
Active
facts
Inactive
facts
Rule and
fact
compiler
Rule
antecedent
subexpressions
Rule and
fact
compiler
Agenda
Scheduler
nterpreter
Active
rules
and
facts
Activation/
deactivation
etarules
Matching
pairs
andidate
rule, data>
Preferences
and
priorities
Prioritized
activations
Selected
action
Incomplete
procedures
Multi-
dimensional
working
memory
Updatesata
xecution
tack
Control
procedures
Next action
Unfinished
actions
Inputs
Outputs
Triggering
data
activations
Delete
completed
activations
ule Memoryule Memoryule Memoryule Memoryule Memoryule Memoryule Memory Fact Memory
Data-flow
network
by partially
evaluated
rule activations
Figure 23: Sophisticated Rule-Based System
The interfaces among the rediscovered components are unchanged from
the simple model except for the two bold lines over which the interpreter
controls activations.
This example illustrates two points. First, in a sophisticated rule-based
system the elements of the simple rule-based system are elaborated in response
to execution characteristics of the particular class of languages being
interpreted. If the design is presented in this way, the original concept is
retained to guide understanding and later maintenance. Second, as the design
is elaborated, different components of the simple model can be elaborated with
different idioms.
Garlan & Shaw: An Introduction to Software Architecture 33
Inactive
rules
Active
rules
Active
facts
Inactive
facts
Rule and
fact
compiler
Rule
antecedent
subexpressions
Rule and
fact
compiler
AgendaScheduler
Interpreter
Active
rules
and
facts
Activation/
deactivation
Metarules
Matching
pairs
Candidate
Preferences
and
priorities
Prioritized
activations
Selected
action
Incomplete
procedures
Multi-
dimensional
working
memory
UpdatesData
Execution
stack
Control
procedures
Next action
Unfinished
actions
Inputs
Outputs
Triggering
data
activations
Delete
completed
activations
Working
Memory
Knowledge
Base
Rule Memory Fact Memory
Rule and data
element
selection
Rule Interpreter
Data-flow
network
by partially
evaluated
rule activations
Figure 23: Simplified Sophisticated Rule-Based System
Note that the rule-based model is itself a design structure: it calls for a set of
rules whose control relations are determined during execution by the state of
the computation. A rule-based system provides a virtual machine—a rule
executor—to support this model.
4.6. Case 6: A Blackboard Globally Recast as Interpreter
The blackboard model of problem solving is a highly structured special case of
opportunistic problem solving. In this model, the solution space is organized
into several application-dependent hierarchies and the domain knowledge is
Garlan & Shaw: An Introduction to Software Architecture 34
partitioned into independent modules of knowledge that operate on
knowledge within and between levels [34]. Figure 4 showed the basic
architecture of a blackboard system and outlined its three major parts:
knowledge sources, the blackboard data structure, and control.
(
Control flow—–Data flow)
Level nLevel n
Condition
Action
Condition
Blackboard
Monitor
Scheduling
Queue
Focus of
Control
Database
Scheduler
Blackboard
Knowledge Sources
Blackboard
Change
Condition Part
Level 3
Level 2
Level 1
Action
Condition
Action
Stimulus
Response Frame
Figure 24: Hearsay-II
The first major blackboard system was the HEARSAY-II speech recognition
system. Nii’s schematic of the HEARSAY-II architecture appears as Figure 24.
The blackboard structure is a six- to eight-level hierarchy in which each level
abstracts information on its adjacent lower level and blackboard elements rep-
resent hypotheses about the interpretation of an utterance. Knowledge sources
correspond to such tasks as segmenting the raw signal, identifying phonemes,
generating word candidates, hypothesizing syntactic segments, and proposing
semantic interpretations. Each knowledge source is organized as a condition
part that specifies when it is applicable and an action part that processes
relevant blackboard elements and generates new ones. The control component
is realized as a blackboard monitor and a scheduler; the scheduler monitors the
blackboard and calculates priorities for applying the knowledge sources to
various elements on the blackboard.
HEARSAY-II was implemented between 1971 and 1976 on DEC PDP-10s;
these machines were not directly capable of condition-triggered control, so it
should not be surprising to find that an implementation provides the
mechanisms of a virtual machine that realizes the implicit invocation
semantics required by the blackboard model.
Garlan & Shaw: An Introduction to Software Architecture 35
Control flow—–Data flow)
Level nLevel n
Condition
Action
Condition
Blackboard
Monitor
Scheduling
Queue
ocus of
ontrol
atabase
Scheduler
Blackboard
Knowledge Sources
Blackboard
Change
Condition Part
Level 3
Level 2
Level 1
Action
Condition
Action
Stimulus
Response Frame
KS
KS
KS
Blackboard
Not
relevant to
blackboard
model
Figure 25: Blackboard View of Hearsay-II
Figure 24 not only elaborates the individual components of Figure 4; it also
adds components for the previously-implicit control component. In the
process, the figure becomes rather complex. This complexity arises because it is
now illustrating two concepts: the blackboard model and realization of that
model by a virtual machine. The blackboard model can be recovered as in
Figure 25 by suppressing the control mechanism and regrouping the
conditions and actions into knowledge sources.
The virtual machine can be seen to be realized by an interpreter using the
assignment of function in Figure 26. Here the blackboard cleanly corresponds
to the current state of the recognition task. The collection of knowledge
sources roughly supplies the pseudocode of the interpreter; however, the
actions also contribute to the interpretation engine. The interpretation engine
includes several components that appear explicitly in Figure 24: the blackboard
monitor, the focus of control database, and the scheduler, but also the actions
of the knowledge sources. The scheduling queue corresponds roughly to the
control state. To the extent that execution of conditions determines priorities,
the conditions contribute to rule selection as well as forming pseudocode.
Here we see a system initially designed with one model (blackboard, a
special form of repository), then realized through a different model
(interpreter). The realization is not a component-by-component expansion as
Garlan & Shaw: An Introduction to Software Architecture 36
in the previous two examples; the view as an interpreter is a different
aggregation of components from the view as blackboard.
( Control flow—–Data flow)
Level nLevel n
Condition
Action
Condition
Blackboard
Monitor
Scheduling
Queue
Focus of
Control
Database
Scheduler
Blackboard
Knowledge Sources
Blackboard
Change
Condition Part
Level 3
Level 2
Level 1
Action
Condition
Action
Stimulus
Response Frame
Program State
(Working Meomory)
Interpretation
Engine
Pseudo-Code
(Knowledge Base)
N
o
te
:
A
ct
io
n
s
a
re
a
ls
o
p
a
rt
o
f
in
te
rp
re
ta
ti
o
n
e
n
g
in
e
Control
State
Figure 26: Interpreter View of Hearsay-II
5. Past, Present, and Future
We have outlined a number of architectural styles and shown how they can be
applied and adapted to specific software systems. We hope that this has
convinced the reader that analysis and design of systems in terms of software
architecture is both viable and worth doing. Further we hope to have made it
clear that an understanding of the emerging vocabulary of architectural styles
is a significant—if not necessary—intellectual tool for the software engineer.
There is, of course, much more to software architecture than we have had
space to cover. In particular, we have said very little about existing results in
the areas of analysis, formal specification, domain-specific architectures,
module interconnection languages, and special-architecture tools.
This is not to say that more work isn’t needed. Indeed, we can expect to see
significant advances in a number of areas including:
• Better taxonomies of architectures and architectural styles.
Garlan & Shaw: An Introduction to Software Architecture 37
• Formal models for characterizing and analyzing architectures.
• Better understanding of the primitive semantic entities from which
these styles are composed.
• Notations for describing architectural designs.
• Tools and environments for developing architectural designs.
• Techniques for extracting architectural information from existing code.
• Better understanding of the role of architectures in the life-cycle process.
These are all areas of active research both in industry and academia. Given
the increasing interest in this emerging field, we can expect that our
understanding of the principles and practice of software architecture will
improve considerably over time. However, as we have illustrated, even with
the basic concepts that we now have in hand, design at the level of software
architecture can provide direct and substantial benefit to the practice of
software engineering.
Acknowledgements
We gratefully acknowledge our many colleagues who have contributed to
the ideas presented in this paper. In particular, we would like to thank Chris
Okasaki, Curtis Scott, and Roy Swonger for their help in developing the course
from which much of this material was drawn. We thank David Notkin, Kevin
Sullivan, and Gail Kaiser for their contribution towards understanding event-
based systems. Rob Allen helped develop a rigorous understanding of the pipe
and filter style. We would like to acknowledge the oscilloscope development
team at Tektronix, and especially Norm Delisle, for their part in demonstrating
the value of domain-specific architectural styles in an industrial context.
Finally, we would like to thank Barry Boehm, Larry Druffel, and Dilip Soni for
their constructive comments on early drafts of the paper.
Bibliography
[1] D. Garlan, M. Shaw, C. Okasaki, C. Scott, and R. Swonger, “Experience
with a course on architectures for software systems,” in Proceedings of the Sixth SEI
Conference on Software Engineering Education, Springer-Verlag, LNCS 376, October 1992.
[2] M. Shaw, “Toward higher-level abstractions for software systems,” in Data & Knowledge
Engineering, vol. 5, pp. 119-128, North Holland: Elsevier Science Publishers B.V., 1990.
[3] M. Shaw, “Heterogeneous design idioms for software architecture,” in Proceedings of the
Sixth International Workshop on Software Specification and Design, IEEE Computer
Society, Software Engineering Notes, (Como, Italy), pp. 158-165, October 25-26, 1991.
[4] M. Shaw, “Software architectures for shared information systems,” in Mind Matters:
Contributions to Cognitive and Computer Science in Honor of Allen Newell, Erlbaum, 1993.
[5] R. Allen and D. Garlan, “A formal approach to software architectures,” in Proceedings of
IFIP’92 (J. van Leeuwen, ed.), Elsevier Science Publishers B.V., September 1992.
Garlan & Shaw: An Introduction to Software Architecture 38
[6] D. Garlan and D. Notkin, “Formalizing design spaces: Implicit invocation mechanisms,” in
VDM’91: Formal Software Development Methods. (Noordwijkerhout, The Netherlands),
pp. 31-44, Springer-Verlag, LNCS 551, October 1991.
[7] D. Garlan, G. E. Kaiser, and D. Notkin, “Using tool abstraction to compose systems,” IEEE
Computer, vol. 25, June 1992.
[8] A. Z. Spector et al., “Camelot: A distributed transaction facility for Mach and the Internet –
an interim report,” Tech. Rep. CMU-CS-87-129, Carnegie Mellon University, June 1987.
[9] M. Fridrich and W. Older, “Helix: The architecture of the XMS distributed file system,”
IEEE Software, vol. 2, pp. 21-29, May 1985.
[10] M. A. Linton, “Distributed management of a software database,” IEEE Software, vol. 4, pp.
70-76, November 1987.
[11] V. Seshadri et al., “Sematic analysis in a concurrent compiler,” in Proceedings of ACM
SIGPLAN ’88 Conference on Programming Language Design and Implementation, ACM
SIGPLAN Notices, 1988.
[12] M. C. Paulk, “The ARC Network: A case study,” IEEE Software, vol. 2, pp. 61-69, May 1985.
[13] M. Chen and R. J. Norman, “A framework for integrated case,” IEEE Software, vol. 9, pp. 18-
22, March 1992.
[14] NIST/ECMA reference model for frameworks of software engineering environments.” NIST
Special Publication 500-201, December 1991.
[15] R. W. Scheifler and J. Gettys, “The X window system,” AACM Transactions on Graphics, vol.
5, pp. 79-109, April 1986.
[16] M. J. Bach, The Design of the UNIX Operating System, ch. 5.12, pp. 111-119. Software
Series, Prentice-Hall, 1986.
[17] N. Delisle and D. Garlan, “Applying formal specification to industrial problems: A
specification of an oscilloscope,” IEEE Software, September 1990.
[18] G. Kahn, “The semantics of a simple language for parallel programming,” Information
Processing, 1974.
[19] M. R. Barbacci, C. B. Weinstock, and J. M. Wing, “Programming at the processor-memory-
switch level,” in Proceedings of the 10th International Conference on Software Engineering,
(Singapore), pp. 19-28, IEEE Computer Society Press, April 1988.
[20] G. E. Kaiser and D. Garlan, “Synthesizing programming environments from reusable
features,” in Software Reusability (T. J. Biggerstaff and A. J. Perlis, eds.), vol. 2, ACM Press,
1989.
[21] W. Harrison, “RPDE: A framework for integrating tool fragments,” IEEE Software, vol. 4,
November 1987.
[22] C. Hewitt, “Planner: A language for proving theorems in robots,” in Proceedings of the First
International Joint Conference in Artificial Intelligence, 1969.
[23] S. P. Reiss, “Connecting tools using message passing in the field program development
environment,” IEEE Software, July 1990.
[24] C. Gerety, “HP Softbench: A new generation of software development tools,” Tech. Rep.
SESD-89-25, Hewlett-Packard Software Engineering Systems Division, Fort Collins,
Colorado, November 1989.
[25] R. M. Balzer, “Living with the next generation operating system,” in Proceedings of the 4th
World Computer Conference, September 1986.
Garlan & Shaw: An Introduction to Software Architecture 39
[26] G. Krasner and S. Pope, “A cookbook for using the model-view-controller user interface
paradigm in Smalltalk-80,” Journal of Object Oriented Programming, vol. 1, pp. 26-49,
August/September 1988.
[27] M. Shaw, E. Borison, M. Horowitz, T. Lane, D. Nichols, and R. Pausch, “Descartes: A
programming-language approach to interactive display interfaces,” Proceedings of
SIGPLAN ’83: Symposium on Programming Language Issues in Software Systems, ACM
SIGPLAN Notices, vol. 18, pp. 100-111, June 1983.
[28] A. N. Habermann and D. S. Notkin, “Gandalf: Software development environments,” IEEE
Transactions on Software Engineering, vol. SE-12, pp. 1117-1127, December 1986.
[29] A. N. Habermann, D. Garlan, and D. Notkin, “Generation of integrated task-specific
software environments,” in CMU Computer Science: A 25th Commemorative (R. F. Rashid,
ed.), Anthology Series, pp. 69-98, ACM Press, 1991.
[30] K. Sullivan and D. Notkin, “Reconciling environment integration and component
independence,” in Proceedings of ACM SIGSOFT90: Fourth Symposium on Software
Development Environments, pp. 22-23, December 1990.
[31] G. R. McClain, ed., Open Systems Interconnection Handbook. New York, NY: Intertext
Publications McGraw-Hill Book Company, 1991.
[32] D. Batory and S. O’Malley, “The design and implementation of hierarchical software
systems using reusable components,” Tech. Rep. TR-91-22, Department of Computer Science,
University of Texas, Austin, June 1991.
[33] H. C. Lauer and E. H. Satterthwaite, “Impact of MESA on system design,” in Proceedings of
the Third International Conference on Software Engineering, (Atlanta, GA), pp. 174-175,
IEEE Computer Society Press, May 1979.
[34] H. P. Nii, “Blackboard systems Parts 1 & 2,” AI Magazine, vol. 7 nos 3 (pp. 38-53) and 4 (pp.
62-69), 1986.
[35] V. Ambriola, P. Ciancarini, and C. Montangero, “Software process enactment in oikos,” in
Proceedings of the Fourth ACM SIGSOFT Symposium on Software Development
Environments, SIGSOFT Software Engineering Notes, (Irvine, CA), pp. 183-192, December
1990.
[36] D. R. Barstow, H. E. Shrobe, and E. Sandewall, eds., Interactive Programming Environments.
McGraw-Hill Book Co., 1984.
[37] G. R. Andrews, “Paradigms for process interaction in distributed programs,” ACM Computing
Surveys, vol. 23, pp. 49-90, March 1991.
[38] A. Berson, Client/Server Architecture. McGraw-Hill, 1992.
[39] E. Mettala and M. H. Graham, eds., The Domain-Specific Software Architecture Program.
No. CMU/SEI-92-SR-9, Carnegie Mellon Software Engineering Institute, June 1992.
[40] D. Harel, “Statecharts: A visual formalism for complex systems,” Science of Computer
Programming, vol. 8, pp. 231-274, 1987.
[41] K. J. Åström and B. Wittenmark, Computer-Controlled Systems Design. Prentice Hall,
second ed., 1990.
[42] D. L. Parnas, “On the criteria to be used in decomposing systems into modules,”
Communications of the ACM, vol. 15, pp. 1053-1058, December 1972.
[43] “PROVOX plus Instrumentation System: System overview,” 1989.
[44] F. Hayes-Roth, ‘Rule-based systems,” Communications of the ACM, vol. 28, pp. 921-932,
September 1985.
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