Deep transfer bug localization

• Main Authors: HUO, Xuan, THUNG, Ferdian, LI, Ming, LO, David, SHI, Shu-Ting
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Cross-project bug localization; transfer learning; deep learning; Computer bugs; Feature extraction; Task analysis; Encoding; Computer languages; Semantics; Data models; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4522; https://doi.org/10.1109/TSE.2019.2920771
• Institution: Singapore Management University

Many projects often receive more bug reports than what they can handle. To help debug and close bug reports, a number of bug localization techniques have been proposed. These techniques analyze a bug report and return a ranked list of potentially buggy source code files. Recent development on bug localization has resulted in the construction of effective supervised approaches that use historical data of manually localized bugs to boost performance. Unfortunately, as highlighted by Zimmermann et al., sufficient bug data is often unavailable for many projects and companies. This raises the need for cross-project bug localization -- the use of data from a project to help locate bugs in another project. To fill this need, we propose a deep transfer learning approach for cross-project bug localization. Our proposed approach named TRANP-CNN extracts transferable semantic features from source project and fully exploits labeled data from target project for effective cross-project bug localization. We have evaluated TRANP-CNN on curated high-quality bug datasets and our experimental results show that TRANP-CNN can locate buggy files correctly at top 1, top 5, and top 10 positions for 29.9%, 51.7%, 61.3% of the bugs respectively, which significantly outperform state-of-the-art bug localization solution based on deep learning and several other advanced alternative solutions considering various standard evaluation metrics.