eCPDP: Early Cross-Project Defect Prediction

Abstract

Cross-project Defect Prediction (CPDP) aims to build a defect prediction model to recognize target project's defective modules by utilizing other source project's historical data. In addition, Transfer Learning (TL) has been widely applied at CPDP to improve prediction performance by alleviating the data distribution discrepancy between the source and the target project. However, existing TL-based CPDP techniques are not applicable at the unit testing phase since they require the entire historical target project data for TL. As a result, they lose a chance of increasing the product's reliability in the unit testing phase by applying the prediction results to identify defects. Thus, the objective of this paper is to apply prediction results at the unit testing phase. To this end, we propose an early CPDP model (eCPDP) which is TL-based CPDP technique using Singular Value Decomposition applicable at the unit testing phase. We compare the performance of eCPDP with state-of-the-art TL-based CPDP techniques on effort-unaware and effort-aware performance metrics over 17 project datasets. Experimental result demonstrates that eCPDP executed during the unit testing stage is one of the best techniques compared to baselines executed after the unit testing stage on both types of metrics. Thus, we show that eCPDP is an applicable CPDP model at the unit testing phase, and it can help practitioners find and fix defects in an earlier phase than other TL-based CPDP techniques.