An empirical analysis of threshold techniques for identifying faulty classes in object oriented software systems

Abstract

The fault-proneness prediction is one of the extensively researched areas in the Software Engineering domain to ameliorate the software quality and to lowers the testing expenses. The existing literature has proved the presence of large amount of work attained the statistical validation of software metrics by utilizing them in the development of fault prediction models, where, both statistical and machine learning techniques were engaged into the construction of the models being capable of identifying faulty and non-faulty classes. On the contrary, the research area involving the investigation of threshold concept has not gained sufficient maturity. An effective threshold technique can assist in the identification of optimal cut-off values of the software metrics which can discriminate the faulty from non-faulty classes with minimal misclassification rate. The idea of threshold calculation can make the application of the existing metrics in software industries, a much easier task. As the developers only need to know the cut-off values which can help them to concentrate on the specific classes that exceeds the computed thresholds. Also, the presence of peculiarity in the software metric index can alert the testers and in turn helps them to disburse the resources systematically. The current study empirically validated and compared the discriminating strength of two threshold techniques, i.e., ROC curve and Alves Rankings, on the publicly available dataset. This study selected twenty object oriented measures for the process of threshold calculation. Furthermore, Wilcoxon signed rank test was used to enquire the classification difference between the aforementioned threshold techniques. The outcome from the statistical analysis revealed the better predictive capability of ROC curve than the Alves Rankings.