Cross-Project Aging Related Bug Prediction

Abstract

In a long running system, software tends to encounter performance degradation and increasing failure rate during execution, which is called software aging. The bugs contributing to the phenomenon of software aging are defined as Aging Related Bugs (ARBs). Lots of manpower and economic costs will be saved if ARBs can be found in the testing phase. However, due to the low presence probability and reproducing difficulty of ARBs, it is usually hard to predict ARBs within a project. In this paper, we study whether and how ARBs can be located through cross-project prediction. We propose a transfer learning based aging related bug prediction approach (TLAP), which takes advantage of transfer learning to reduce the distribution difference between training sets and testing sets while preserving their data variance. Furthermore, in order to mitigate the severe class imbalance, class imbalance learning is conducted on the transferred latent space. Finally, we employ machine learning methods to handle the bug prediction tasks. The effectiveness of our approach is validated and evaluated by experiments on two real software systems. It indicates that after the processing of TLAP, the performance of ARB bug prediction can be dramatically improved.