Studying Aging-Related Bug Prediction Using Cross-Project Models

Abstract

In long running systems, software tends to encounter performance degradation and increasing failure rate during execution. This phenomenon has been named software aging, which is caused by aging-related bugs (ARBs). Testing resource allocation can be optimized by identifying ARB-prone modules with ARB prediction. However, due to the low presence and reproducing difficulty of ARBs, it is usually hard to collect sufficient training data to carry out within-project ARB prediction. In this paper, we propose an approach named transfer learning based aging-related bug prediction (TLAP) to perform cross-project ARB prediction. TLAP first takes advantage of transfer learning to reduce distribution difference between training and testing project. Then, class imbalance learning is conducted to mitigate the severe class imbalance between ARB-prone and ARB-free modules. Finally, machine learning methods are used to handle bug prediction tasks. The effectiveness of this approach is validated and evaluated by nine groups of experiments on real software systems. Major conclusions from the experiments include the following: first, TLAP improves cross-project ARB prediction on average compared with traditional machine learning methods; second, utilizing information from multiple-projects can further improve the prediction performance on average. In the best case, it outperforms within-project prediction; third, the number of ARB-prone files and distribution similarity can influence TLAP performance.