A Cross-Project Aging-Related Bug Prediction Approach Based on Joint Probability Domain Adaptation and k-means SMOTE
Dimeng Li, Mengting Liang, Bin Xu, Xiao Yu, Junwei Zhou, Jianwen Xiang · 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C) · 2021
In long-running systems, the phenomenon of performance degradation and failure rate increase caused by Aging-Related Bugs (ARBs) is known as software aging. Because of the low presence and reproducing difficulty of ARBs, collecting enough training data to predict ARBs in a project is not easy. Thus, cross-project ARB prediction has been proposed. There are two main challenges in cross-project ARB prediction, namely distribution differences and severe class imbalance. As for the first challenge, existing cross-project ARB prediction approaches only focus on the transferability between domains while ignoring the discriminability between classes. As for the second challenge, existing approaches only consider the imbalance between the classes while ignoring the within-class imbalance problem. To solve these problems, a cross-project ARB prediction approach based on Joint Probability Domain Adaptation (JPDA) and k- means SMOTE (KS), called JPKS, is proposed. JPDA is used to consider the transferability and discriminability simultaneously, and KS solves the within-class and between-class imbalance problems. Experiments are conducted on two natural software systems to verify the performance of JPKS. The results show that JPKS can improve the performance of ARB prediction.