A fuzzy logic framework to improve the performance and interpretation of rule-based quality prediction models for OO software

Current object-oriented (OO) software systems must satisfy new requirements that include quality aspects. These, contrary to functional requirements, are difficult to determine during the test phase of a project. Predictive and estimation models offer an interesting solution to this problem. This paper describes an original approach to build rule-based predictive models that are based on fuzzy logic and that enhance the performance of classical decision trees. The approach also attempts to bridge the cognitive gap that may exist between the antecedent and the consequent of a rule by turning the latter into a chain of sub rules that account for domain knowledge. The whole framework is evaluated on a set of OO applications.