Software aging results from aging-related bugs (ARBs) in long-running systems, that usually causes performance decline and system crashes. Since collecting ARB data is challenging due to its scarcity, it hinders the development of effective prediction models. Moreover, existing cross-project ARB prediction methods often ignore project-specific distribution differences and neglect class imbalance and overlap issues between ARB and non-ARB classes. In this paper, a hybrid approach that combines the balanced distribution adaptation (BDA), the improved subclass discriminant analysis (ISDA), and the self-paced ensemble under-sampling (SPE) techniques, called BISP in short, is proposed to address the aforementioned problems. The main idea behind BISP is first to use BDA to adaptively reduce the difference of projects' marginal distribution and conditional distribution, and then employ ISDA and SPE to alleviate the severe class imbalance together with class overlap. Experimental results obtained for six classifiers and six cross-project datasets show that compared with the state-of-the-art approaches TLAP and JDA-ISDA based on transfer learning, BISP improves the average balance by 34.8% and 2.3% and improves the average AUC by 26.5% and 8.4%, respectively. Compared with the deep learning approach SRLA, BISP can improve the average balance value by 5.1%.

Software aging, which is caused by Aging-Related Bugs (ARBs), refers to the phenomenon of performance degradation and eventual crash in long running systems. In order to discover and remove ARBs, ARB prediction is proposed. However, due to the low presence and reproducing difficulty of ARBs, it is usually difficult to collect sufficient ARB data within a project. Therefore, cross-project ARB prediction is proposed as a solution to build the target project's ARB predictor by using the labeled data from the source project. A key point for cross-project ARB prediction is to reduce distribution difference between source and target project. However, existing approaches mainly focus on the marginal distribution difference while somehow overlook the conditional distribution difference, and they mainly use random oversampling to alleviate the class imbalance which may lead to overfitting. To address these problems, we propose a new crossproject ARB prediction approach based on Joint Distribution Adaptation (JDA) and Improved Subclass Discriminant Analysis (ISDA), called JDA-ISDA. The key idea of JDA-ISDA is first to use JDA to reduce the marginal distribution and conditional distribution difference jointly and then apply ISDA to alleviate the severe class imbalance problem. A set of experiments are carried out on two large open-source projects with six different machine learning (ML) classifiers. The experimental results demonstrate that compared with the state-of-the-art Transfer Learning based Aging-related bug Prediction (TLAP) and Supervised Representation Learning Approach (SRLA), JDA-ISDA is much more robust to different ML classifiers than TLAP, and the average improvement in terms of the balance value can be achieved up to 31.8%, and JDA-ISDA also outperforms TLAP and SRLA on average when logistic regression is chosen as the classifier for best performance prediction.