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Expert system verification and validation: a survey and tutorial

Abstract

Assuring the quality of an expert system is critical. A poor quality system may make costly errors resulting in considerable damage to the user or owner of the system, such as financial loss or human suffering. Hence verification and validation, methods and techniques aimed at ensuring quality, are fundamentally important.

This paper surveys the issues, methods and techniques for verifying and validating expert systems. Approaches to defining the quality of a system are discussed, drawing upon work in both computing and the model building disciplines, which leads to definitions of verification and validation and the associated concepts of credibility, assessment and evaluation. An approach to verification based upon the detection of anomalies is presented, and related to the concepts of consistency, completeness, correctness and redundancy. Automated tools for expert system verification are reviewed.

Considerable attention is then given to the issues in structuring the validation process, particularly the establishment of the criteria by which the system is judged, the need to maintain objectivity, and the concept of reliability. This is followed by a review of validation methods for validating both the components of a system and the system as a whole, and includes examples of some useful statistical methods. Management of the verification and validation process is then considered, and it is seen that the location of methods for verification and validation in the development life-cycle is of prime importance.

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Abbreviations

D.2.4 [Software Engineering]:

Program Verification — Validation

D.2.5 [Software Engineering]:

Testing and Debugging

1.2 [Artificial Intelligence]:

Applications and Expert Systems

K.6.1 [Management of Computers and Information System]:

Project and People Management — Life Cycle

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O'Keefe, R.M., O'Leary, D.E. Expert system verification and validation: a survey and tutorial. Artif Intell Rev 7, 3–42 (1993). https://doi.org/10.1007/BF00849196

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Key Words

  • expert systems
  • knowledge-based systems
  • verification
  • validation
  • testing
  • evaluation
  • credibility
  • assessment
  • development
  • life cycle
  • statistics