Applying Mining Schemes to Software Fault Prediction: A Proposed Approach Aimed at Test Cost Reduction

A.A. Shahrjooi Haghighi, M. Abbasi Dezfuli, S.M. Fakhrahmad

Abstract: Software fault prediction based on mining of code and design metrics has been considered by many researchers. Fault detection systems predict faults by using software metrics and data mining techniques. Various classifiers have already been used in this case; however Naïve Bayes classifier is the most commonly used. According to the results of a study performed by Lessman, no significant performance difference could be detected among the top 17 classifiers. In this paper, we will extend that study by examining the performance of 37 different classifiers in fault detection systems. We will review the results and aim to choose an appropriate classifier (Bagging) which depicts a higher performance and accuracy compared to the others. Finally, we propose a fault detection system with higher performance which manages to decrease the cost of software fault detection simultaneously. We investigate our classifier selection by evaluating the methods on a number of other datasets. Our results indicate that Bagging classifier has the highest performance in fault detection.