Software defect prediction in imbalanced data sets using unbiased support vector machine

Abstract

In the software assurance process, it is crucial to prevent a program with defected modules to be published to users since it can save the maintenance cost and increase software quality and reliability. There were many prior attempts to automatically capture errors by employing conventional classification techniques, e.g., Decision Tree, k-NN, Naive Bayes, etc. However, their detection performance was limited due to the imbalanced issue since the number of defected modules is very small comparing to that of non-defected modules. This paper aims to achieve high prediction rate by employing unbiased SVM called “R-SVM,” our version of SVM tailored to domains with imbalanced classes. The experiment was conducted in the NASA Metric Data Program (MDP) data set. The result showed that our proposed system outperformed all of the major traditional approaches.