Multiple‐components weights model for cross‐project software defect prediction

Abstract

Software defect prediction (SDP) technology is receiving widely attention and most of SDP models are trained on data from the same project. However, at an early phase of the software lifecycle, there are little to no within-project training data to learn an available supervised defect-prediction model. Thus, cross-project defect prediction (CPDP), which is learning a defect predictor for a target project by using labelled data from a source project, has shown promising value in SDP. To better perform the CPDP, most current studies focus on filtering instances or selecting features to weaken the impact of irrelevant cross-project data. Instead, the authors propose a novel multiple-components weights (MCWs) learning model to analyse the varying auxiliary power of multiple components in a source project to construct a more precise ensemble classifiers for a target project. By combining the MCW model with kernel mean matching algorithm, their proposed approach adjusts the source-instance weights and source-component weights to jointly alleviate the negative impacts of irrelevant cross-project data. They conducted comprehensive experiments by employing 15 real-world datasets to demonstrate the advantages and effectiveness of their proposed approach.