A Decision Tree Regression based Approach for the Number of Software Faults Prediction

Abstract

Software fault prediction is an important activity to make software quality assurance (SQA) process more efficient, economic and targeted. Most of earlier works related to software fault prediction have focused on classifying software modules as faulty or non-faulty. However, prediction of the number of faults in a given software module is not adequately investigated. In this paper, we explore the capability of decision tree regression (DTR) for the number of faults prediction in two different scenarios, intra-release prediction and inter-releases prediction for the given software system. The experimental study is performed over five open-source software projects with their nineteen releases collected from the PROMISE data repository. The predictive accuracy of DTR is evaluated using absolute error and relative error, prediction at level l and goodness-of-t measure. The results show that decision tree regression produced significant prediction accuracy for the number of faults prediction in both the considered scenarios. The relative comparison of intra-release and inter-releases fault prediction shows that intra-project prediction produced better accuracy compared to inter-releases prediction across all the datasets