Empirical Validate C&K Suite for Predict Fault-Proneness of Object-Oriented Classes Developed Using Fuzzy Logic

Empirical validation of software metrics suites to predict fault proneness in object-oriented (OO) components is essential to ensure their accuracy in practical industrial. In this paper, we empirically validate the Chidamber and Kemerer (CK) metrics suite metrics for their ability to predict software quality in terms of fault-proneness: we explore the ability of these metrics suites to predict fault-prone classes using defect data for six versions of Rhino, an open-source implementation of JavaScript written in Java. We conclude that the C&K suite contain similar components and produce statistical models that are effective in detecting error-prone classes. Analyzing Fuzzy Logic models across six Rhino versions indicates these models may be useful in assessing quality in OO classes produced using modern highly iterative or agile software development processes. Keywordsfault-prone; fuzzy logic; software quality; prediction model