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.
Fri 10 JulDisplayed time zone: (UTC) Coordinated Universal Time change
15:00 - 16:00 | A21-Testing and Debugging 3Journal First / Technical Papers at Silla Chair(s): Tingting Yu University of Kentucky | ||
15:00 12mTalk | Schrödinger's Security: Opening the Box on App Developers' Security RationaleTechnical Technical Papers Dirk van der Linden University of Bristol, Pauline Anthonysamy Google Inc., Bashar Nuseibeh The Open University (UK) & Lero (Ireland), Thein Tun , Marian Petre The Open University, Mark Levine Lancaster University, John Towse Lancaster University, Awais Rashid University of Bristol, UK | ||
15:12 8mTalk | Smart Greybox FuzzingJ1 Journal First Van-Thuan Pham Monash University, Marcel Böhme Monash University, Andrew Santosa National University of Singapore, Alexandru Răzvan Căciulescu UiPath, Abhik Roychoudhury National University of Singapore, Singapore | ||
15:20 8mTalk | Deep Transfer Bug LocalizationJ1 Journal First Xuan Huo Nanjing University, Ferdian Thung Singapore Management University, Ming Li Nanjing University, David Lo Singapore Management University, Shu-Ting Shi Nanjing University | ||
15:28 8mTalk | A Benchmark-Based Evaluation of Search-Based Crash ReproductionJ1 Journal First Mozhan Soltani Leiden University, Pouria Derakhshanfar Delft University of Technology, Xavier Devroey Delft University of Technology, Arie van Deursen Delft University of Technology Link to publication DOI Pre-print Media Attached | ||
15:36 12mTalk | An Investigation of Cross-Project Learning in Online Just-In-Time Software Defect PredictionTechnical Technical Papers Sadia Tabassum University of Birmingham, UK, Leandro Minku University of Birmingham, UK, Danyi Feng XiLiu Tech, George Cabral Universidade Federal Rural de Pernambuco, Liyan Song University of Birmingham | ||
15:48 8mTalk | An Empirical Study of the Long Duration of Continuous Integration BuildsJ1 Journal First Taher A Ghaleb Queen's University, Daniel Alencar Da Costa University of Otago, Ying Zou Queen's University, Kingston, Ontario Link to publication DOI Pre-print | ||