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Software Complexity: Bringing Order to Chaos


Categories of Analysis and Design Methods

We find it useful to distinguish between the terms method and methodology. A method is a disciplined procedure for generating a set of models that describe various aspects of a software system under development, using some well-defined notation. A methodology is a collection of methods applied across the software development lifecycle and unified by process, practices, and some general, philosophical approach. Methods are important for several reasons. Foremost, they instill a discipline into the development of complex software systems. They define the products that serve as common vehicles for communication among the members of a development team. Additionally, methods define the milestones needed by management to measure progress and to manage risk.

Methods have evolved in response to the growing complexity of software systems. In the early days of computing, one simply did not write large programs because the capabilities of our machines were greatly limited. The dominant constraints in building systems were then largely due to hardware: Machines had small amounts of main memory, programs had to contend with considerable latency within secondary storage devices such as magnetic drums, and processors had cycle times measured in the hundreds of microseconds. In the 1960s and 1970s the economics of computing began to change dramatically as hardware costs plummeted and computer capabilities rose. As a result, it was more desirable and now finally economical to automate more and more applications of increasing complexity. High-order programming languages entered the scene as important tools. Such languages improved the productivity of the individual developer and of the development team as a whole, thus ironically pressuring us to create software systems of even greater complexity.

Many design methods were proposed during the 1960s and 1970s to address this growing complexity. The most influential of them was top-down structured design, also known as composite design. This method was directly influenced by the topology of traditional high-order programming languages, such as FORTRAN and COBOL. In these languages, the fundamental unit of decomposition is the subprogram, and the resulting program takes the shape of a tree in which subprograms perform their work by calling other subprograms. This is exactly the approach taken by top-down structured design: One applies algorithmic decomposition to break a large problem down into smaller steps.

Since the 1960s and 1970s, computers of vastly greater capabilities have evolved. The value of structured design has not changed, but as Stein observes, "Structured programming appears to fall apart when applications exceed 100,000 lines or so of code" . Dozens of design methods have been proposed, many of them invented to deal with the perceived shortcomings of top-down structured design. The more interesting and successful design methods are cataloged by Peters, by Yau and Tsai, and in a comprehensive survey by Teledyne Brown Engineering. Perhaps not surprisingly, many of these methods are largely variations on a similar theme. Indeed, as Sommerville suggests, most methods can be categorized as one of three kinds:

  • Top-down structured design

  • Data-driven design

  • Object-oriented design

Top-down structured design is exemplified by the work of Yourdon and Constantine, Myers, and Page-Jones. The foundations of this method derive from the work of Wirth and Dahl, Dijkstra, and Hoare; an important variation on structured design is found in the design method of Mills, Linger, and Hevner. Each of these variations applies algorithmic decomposition. More software has probably been written using these design methods than with any other. Nevertheless, structured design does not address the issues of data abstraction and information hiding, nor does it provide an adequate means of dealing with concurrency. Structured design does not scale up well for extremely complex systems, and this method is largely inappropriate for use with object-based and object-oriented programming languages.

Data-driven design is best exemplified by the early work of Jackson and the methods of Orr. In this method, mapping system inputs to outputs derives the structure of a software system. As with structured design, data-driven design has been successfully applied to a number of complex domains, particularly information management systems, which involve direct relationships between the inputs and outputs of the system but require little concern for time-critical events.

The underlying concept of object-oriented analysis is that one should model software systems as collections of cooperating objects, treating individual objects as instances of a class within a hierarchy of classes. Object-oriented analysis and design directly reflects the topology of high-order programming languages such as Smalltalk, Object Pascal, C++, the Common Lisp Object System (CLOS), Ada, Eiffel, Python, Visual C#, and Java.


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