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 the 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.
|Number of pages||8|
|Journal||Proceedings - IEEE Computer Society's International Computer Software and Applications Conference|
|Publication status||Published - 2002|
|Event||26th Annual International Computer Software and Applications Conference - Oxford, United Kingdom|
Duration: Aug 26 2002 → Aug 29 2002
ASJC Scopus subject areas
- Computer Science Applications