Complexity metrics for rule-based expert systems

The increasing application of rule-based expert systems has led to the urgent need to quantitatively measure their quality, especially the maintainability which is harder and more expensive than that of conventional software because of the dynamic and evolutionary features of rules. One of the main factors that affect the maintainability of rule-based expert systems is their complexity; but so far little effort has been devoted to measure it. The paper investigates several complexity metrics for rule-based expert systems, and presents some evaluation methods based on statistical testing, analysis and comparison to assess the validity of these metrics. 71 rule-based expert systems are collected as test data from different application areas. The results reveal that a properly defined complexity metric, like our proposed RC, can be used as an effective means to measure the complexity of rule-based expert systems