Practical Program Repair via Preference-based Ensemble Strategy
To date, over 40 Automated Program Repair (APR) tools have been designed with varying bug-fixing strategies, which have been demonstrated to have complementary performance in terms of being effective for different bug classes. Intuitively, it should be feasible to improve the overall bug-fixing performance of APR via assembling existing tools. Unfortunately, simply invoking all available APR tools for a given bug can result in unacceptable costs on APR execution as well as on patch validation (via expensive testing). Therefore, while assembling existing tools is appealing, it requires an efficient strategy to reconcile the need to fix more bugs and the requirements for practicality. In light of this problem, we propose a Preference-based Ensemble Program Repair framework (P-EPR), which seeks to effectively rank APR tools for repairing different bugs. P-EPR is the first non-learning-based APR ensemble method that is novel in its exploitation of repair patterns as a major source of knowledge for ranking APR tools and its reliance on a dynamic update strategy that enables it to immediately exploit and benefit from newly derived repair results. Experimental results show that P-EPR outperforms existing strategies significantly both in flexibility and effectiveness.
Wed 17 AprDisplayed time zone: Lisbon change
14:00 - 15:30 | Program Repair 2Journal-first Papers / Research Track / Software Engineering in Practice at Pequeno Auditório Chair(s): Xiang Gao Beihang University | ||
14:00 15mTalk | Practical Program Repair via Preference-based Ensemble Strategy Research Track Wenkang Zhong State Key Laboratory for Novel Software and Technology, Nanjing University, 22 Hankou Road, Nanjing, China, Chuanyi Li Nanjing University, Kui Liu Huawei, Tongtong Xu Huawei, Jidong Ge Nanjing University, Tegawendé F. Bissyandé University of Luxembourg, Bin Luo Nanjing University, Vincent Ng Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688 | ||
14:15 15mTalk | Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data Research Track Martin Tappler TU Graz; Silicon Austria Labs, Andrea Pferscher Institute of Software Technology, Graz University of Technology , Bernhard Aichernig Graz University of Technology, Bettina Könighofer Graz University of Technology | ||
14:30 15mTalk | BinAug: Enhancing Binary Similarity Analysis with Low-Cost Input Repairing Research Track WONG Wai Kin Hong Kong University of Science and Technology, Huaijin Wang Hong Kong University of Science and Technology, Li Zongjie Hong Kong University of Science and Technology, Shuai Wang The Hong Kong University of Science and Technology | ||
14:45 15mTalk | Constraint Based Program Repair for Persistent Memory Bugs Research Track | ||
15:00 15mTalk | User-Centric Deployment of Automated Program Repair at Bloomberg Software Engineering in Practice David Williams University College London, James Callan UCL, Serkan Kirbas Bloomberg LP, Sergey Mechtaev University College London, Justyna Petke University College London, Thomas Prideaux-Ghee Bloomberg LP, Federica Sarro University College London | ||
15:15 7mTalk | AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities Journal-first Papers Michael Fu Monash University, Kla Tantithamthavorn Monash University, Trung Le Monash University, Australia, Yuki Kume Monash University, Van Nguyen Monash University, Dinh Phung Monash University, Australia, John Grundy Monash University Link to publication DOI Pre-print | ||