Cost-Sensitive Learner on Hybrid SMOTE-Ensemble Approach to Predict Software Defects

Abstract

A software defect is a mistake in a computer program or system that causes to have incorrect or unexpected results, or to behave in unintended ways. Machine learning methods are helpful in software defect prediction, even though with the challenge of imbalanced software defect distribution, such that the non-defect modules are much higher than defective modules. In this paper we introduce an enhancement for the most resent hybrid SMOTE-Ensemble approach to deal with software defects problem, utilizing the Cost-Sensitive Learner (CSL) to improve handling imbalanced distribution issue. This paper utilizes four public available datasets of software defects with different imbalanced ratio, and provides comparative performance analysis with the most resent powerful hybrid SMOTE-Ensemble approach to predict software defects. Experimental results show that utilizing multiple machine learning techniques to cope with imbalanced datasets will improve the prediction of software defects. Also, experimental results reveal that cost-sensitive learner performs very well with highly imbalanced datasets than with low imbalanced datasets.