NegCPARBP: Enhancing Privacy Protection for Cross-Project Aging-Related Bug Prediction Based on Negative Database.
Abstract
The emergence of Aging-Related Bugs (ARBs) poses a significant challenge to software systems, resulting in performance degradation and increased error rates in resource-intensive systems. Consequently, numerous ARB prediction methods have been developed to mitigate these issues. However, in scenarios where training data is limited, the effectiveness of ARB prediction is often suboptimal. To address this problem, Cross-Project Aging-Related Bug Prediction (CPARBP) is proposed, which utilizes data from other projects (i.e., source projects) to train a model aimed at predicting potential ARBs in a target project. However, the use of source-project data raises privacy concerns and discourages companies from sharing their data. Therefore, we propose a method called Cross-Project Aging-Related Bug Prediction based on Negative Database (NegCPARBP) for privacy protection. NegCPARBP first converts the feature vector of a software file into a binary string. Second, the corresponding Negative DataBase (NDB) is generated based on this binary string, containing data that is significantly more expressive from the original feature vector. Furthermore, to ensure more accurate prediction of ARBprone and ARB-free files based on privacy-protected data (i.e., maintain the data utility), we propose a novel negative database generation algorithm that captures more information about important features, using information gain as a measure. Finally, NegCPARBP extracts a new feature Received 18 March 2024; revised 22 January 2025; accepted 24 February 2025. Date of publication 6 March 2025; date of current version 27 June 2025. This work was supported in part by the National Natural Science Foundation of China under Grant 61806151, in part by the National Key Research and Development Program under Grant 2022YFB3104001 and Grant 2022YFC3321102, and in part by the Natural Science Foundation of Chongqing City under Grant CSTC2021JCYJ-MSXMX0002 and Grant CSTC2021JCYJMSXMX1115. (Corresponding author: Xiao Yu.) Dongdong Zhao is with the School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430062, China, and also with the Chongqing Research Institute, W