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There has been some research effort into and development of a number of techniques to support V&V of expert system, and in identifying the most appropriate phases in the development to which they may be applied. Thus, opportunities to perform verification and validation occur at various stages during the development of the expert system. In the waterfall approach to software development, V&V can be carried out at the end of each phase of development.

The equivalent waterfall model for expert systems (Figure 11) is similar in a way but presents a number of particularities (Coenen and Bench-Capon, 1993). These include:

  • System analysis. Is not always clear that the same principles used in conventional software can be used for them too, since the estimates of costs, time, and benefits for an expert system are very unreliable.
  • Requirements analysis and definition. It is difficult to provide clearly the requirements of the system and in many cases this stage is neglected because many expert system applications have used the prototyping life-cycle paradigm.
  • Software specification and design. The notions of data structures and procedures are not relevant, and the KB is the focal point, with the design concentrating on the choice of representation for the knowledge and an inference engine to manipulate the knowledge.
  • Implementation. The implementation is more complicated since the specification and the design are not so detailed as in the case of conventional software. The expert systems are normally implemented using languages such as PROLOG, LISP, or a shell and are generally application specific, which makes it difficult to apply standards in order to reuse the code or perform major changes to the KB software product.
  • Testing. The testing stage is rather vague and in many cases is integrated into the stage of eliciting and refining knowledge from the expert.
  • Maintenance. There are attempts to adapt Swanson's classification system for traditional software system maintenance.

More or less serious criticisms of the traditional waterfall model have led to alternative models such as rapid prototyping and spiral modeling, which have been developed to improve or replace the waterfall model. These models have been adopted by developers of expert systems as well. The software life cycle for spiral modeling is somewhat similar to the prototype model. In the analysis stage, the software development team specifies in the requirements documents of the software project the validation procedures, risk identification, a number of KB requirements, constraints on the computer memory required by the KB, schedule, resources, and an overall test strategy, including a verification and validation test plan.

The attempts to define life-cycle models for expert system construction has led to the possibility of coupling the V&V activities to the specific stages in the development. Thus, V&V techniques are not stand-alone, but part of this methodology. However, important to notice is that the focus of V&V is not the same at each stage since the objectives of each are stage different. An example of such a mapping between different development stages and the focus of V&V in each of them is summarized in Table 3.

Such a mapping gives guidance to what V&V can be done when. Essentially, V&V is spread over four stages: (1) requirement analysis, (2) knowledge acquisition, (3) knowledge specification and refinement and (4) implementation.


TABLE 3
Mapping Focus of V&V Methods to the Development Stages
 
Life cycle Focus of V&V
Requirement analysis and initial knowledge acquisition The focus is on achieving satisfactory requirements and consistent initial specifications. The use of traditional V&V techniques may not be applicable since an expert system is knowledge intensive rather than data intensive. The initial requirements are primarily related to questions about what kind of problems can be solved under which environments. Inconsistencies detected at this stage are resolved by supplying additional knowledge. The human expert is directly involved in the process of discarding or reflecting modifications in the initial knowledge specification.
Knowledge specification V&V activities at this level exploit the structure of the conceptual model, focusing on validating the knowledge specification. During such activities, knowledge analysis can reveal potential errors. A failure to prove a desired property is a trigger to complete the knowledge specification. The human expert proposes a set of possible repairs.
Refinement of knowledge specification The relation between knowledge analysis and refinement relies on the fact that the latter follows the former. The analysis of desired properties guides the refinement process. The V&V activities focus on performing correct refinements, thus ensuring consistency between two consecutive levels of specification.
Implementation The focus is checking the implemented system for internal consistency, also demonstrating the compliance with the specification. Automated tools may greatly help in performing V&V activities.

3.4. VERIFICATION AND VALIDATION SYSTEMS

To test for the anomalies and errors identified in previous chapters, many verification and validation systems have been built over the last decade. In the following, we will survey some of them, mentioning that the survey is necessarily incomplete, and it is based on depth rather than breadth. Our primary motivation in choosing these particular systems is that they provide a good cross-section of available verification and validation types of methods and tools. These systems along with others can be found in Vermesan and Bench-Capon (1995) and Preece et al. (1992), where pointers to the developers of the systems are also provided.


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