A novel cross-project software defect prediction algorithm based on transfer learning

Abstract

Software Defect Prediction (SDP) technology is an effective tool for improving software system quality that has attracted much attention in recent years. However, the prediction of cross-project data remains a challenge for the traditional SDP method due to the different distributions of the training and testing datasets. Another major difficulty is the class imbalance issue that must be addressed in Cross-Project Defect Prediction (CPDP). In this work, we propose a transfer-leaning algorithm (TSboostDF) that considers both knowledge transfer and class imbalance for CPDP. The experimental results demonstrate that the performance achieved by TSboostDF is better than those of existing CPDP methods.