Sftware Engineering-Analysis Principles

Over the past two decades, a large number of analysis modeling methods have been developed. Investigators have identified analysis problems and their causes and have developed a variety of modeling notations and corresponding sets of heuristics to overcome them. Each analysis method has a unique point of view. However, all analysis methods are related by a set of operational principles:

1. The information domain of a problem must be represented and understood.
2. The functions that the software is to perform must be defined.
3. The behavior of the software (as a consequence of external events) must be represented.
4. The models that depict information, function, and behavior must be partitioned in a manner that uncovers detail in a layered (or hierarchical) fashion.
5. The analysis process should move from essential information toward implementation detail.

By applying these principles, the analyst approaches a problem systematically. The information domain is examined so that function may be understood more completely. Models are used so that the characteristics of function and behavior can be communicated in a compact fashion. Partitioning is applied to reduce complexity. Essential and implementation views of the software are necessary to accommodate the logical constraints imposed by processing requirements and the physical constraints imposed by other system elements.

In addition to these operational analysis principles, Davis suggests a set of guiding principles for requirements engineering:

Understand the problem before you begin to create the analysis model. There is a tendency to rush to a solution, even before the problem is understood. This often leads to elegant software that solves the wrong problem!

Develop prototypes that enable a user to understand how human/machine interaction will occur. Since the perception of the quality of software is often based on the perception of the “friendliness” of the interface, prototyping (and the iteration that results) are highly recommended.

Record the origin of and the reason for every requirement. This is the first step in establishing traceability back to the customer.

Use multiple views of requirements. Building data, functional, and behavioral models provide the software engineer with three different views. This reduces the likelihood that something will be missed and increases the likelihood that inconsistency will be recognized.

Rank requirements. Tight deadlines may preclude the implementation of every software requirement. If an incremental process model is applied, those requirements to be delivered in the first increment must be identified.

Work to eliminate ambiguity. Because most requirements are described in a natural language, the opportunity for ambiguity abounds. The use of formal technical reviews is one way to uncover and eliminate ambiguity.

A software engineer who takes these principles to heart is more likely to develop a software specification that will provide an excellent foundation for design.

The Information Domain

All software applications can be collectively called data processing. Interestingly, this term contains a key to our understanding of software requirements. Software is built to process data, to transform data from one form to another; that is, to accept input, manipulate it in some way, and produce output. This fundamental statement of objective is true whether we build batch software for a payroll system or real-time embedded software to control fuel flow to an automobile engine.

It is important to note, however, that software also processes events. An event represents some aspect of system control and is really nothing more than Boolean data—it is either on or off, true or false, there or not there. For example, a pressure sensor detects that pressure exceeds a safe value and sends an alarm signal to monitoring software. The alarm signal is an event that controls the behavior of the system. Therefore, data (numbers, text, images, sounds, video, etc.) and control (events) both reside within the information domain of a problem.

The first operational analysis principle requires an examination of the information domain and the creation of a data model. The information domain contains three different views of the data and control as each is processed by a computer program: (1) information content and relationships (the data model), (2) information flow, and (3) information structure. To fully understand the information domain, each of these views should be considered.

Information content represents the individual data and control objects that constitute some larger collection of information transformed by the software. For example, the data object, paycheck, is a composite of a number of important pieces of data: the payee's name, the net amount to be paid, the gross pay, deductions, and so forth. Therefore, the content of paycheck is defined by the attributes that are needed to create it. Similarly, the content of a control object called system status might be defined by a string of bits. Each bit represents a separate item of information that indicates whether or not a particular device is on- or off-line.

Data and control objects can be related to other data and control objects. For example, the data object paycheck has one or more relationships with the objects timecard, employee, bank, and others. During the analysis of the information domain, these relationships should be defined.

Information flow represents the manner in which data and control change as each moves through a system.Input objects are transformed to intermediate information (data and/or control), which is further transformed to output. Along this transformation path (or paths), additional information may be introduced from an existing data store (e.g., a disk file or memory buffer). The transformations applied to the data are functions or subfunctions that a program must perform. Data and control that move between two transformations (functions) define the interface for each function.
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