TrickyBugs: A Dataset of Corner-case Bugs in Plausible Programs
We call a program that passes existing tests but still contains bugs as a buggy plausible program. Bugs in such a program can bypass the testing environment and enter the production environment, causing unpredictable consequences. Therefore, discovering and fixing such bugs is a fundamental and critical problem. However, no existing bug dataset is purposed to collect this kind of bug, posing significant obstacles to relevant research. To address this gap, we introduce TrickyBugs, a bug dataset with 3,043 buggy plausible programs sourced from human-written submissions of 324 real-world competition coding tasks. We identified the buggy plausible programs from approximately 400,000 submissions, and all the bugs in TrickyBugs were not previously detected. We hope that TrickyBugs can effectively facilitate research in the fields of automated program repair, fault localization, test generation, and test adequacy.
Mon 15 AprDisplayed time zone: Lisbon change
11:00 - 12:30 | Defects, Bugs and IssuesTechnical Papers / MSR Awards / Social Events / Tutorials / Data and Tool Showcase Track / Mining Challenge / Registered Reports / Industry Track / MIP Award / Vision and Reflection / Keynotes at Grande Auditório Chair(s): Wesley Assunção North Carolina State University | ||
11:00 12mTalk | Enhancing Performance Bug Prediction Using Performance Code Metrics Technical Papers Guoliang Zhao Computer Science of Queen's University, Stefanos Georgio , Safwat Hassan University of Toronto, Canada, Ying Zou Queen's University, Kingston, Ontario, Derek Truong IBM Canada, Toby Corbin IBM UK | ||
11:12 12mTalk | CrashJS: A NodeJS Benchmark for Automated Crash Reproduction Technical Papers Philip Oliver Victoria University of Wellington, Jens Dietrich Victoria University of Wellington, Craig Anslow Victoria University of Wellington, Michael Homer Victoria University of Wellington | ||
11:24 12mTalk | An Empirical Study on Just-in-time Conformal Defect Prediction Technical Papers Xhulja Shahini paluno - University of Duisburg-Essen, Andreas Metzger University of Duisburg-Essen, Klaus Pohl | ||
11:36 12mTalk | Fine-Grained Just-In-Time Defect Prediction at the Block Level in Infrastructure-as-Code (IaC) Technical Papers Mahi Begoug , Moataz Chouchen ETS, Ali Ouni ETS Montreal, University of Quebec, Eman Abdullah AlOmar Stevens Institute of Technology, Mohamed Wiem Mkaouer University of Michigan - Flint | ||
11:48 4mTalk | TrickyBugs: A Dataset of Corner-case Bugs in Plausible Programs Data and Tool Showcase Track Kaibo Liu Peking University, Yudong Han Peking University, Yiyang Liu Peking University, Zhenpeng Chen Nanyang Technological University, Jie M. Zhang King's College London, Federica Sarro University College London, Gang Huang Peking University, Yun Ma Peking University | ||
11:52 4mTalk | GitBugs-Java: A Reproducible Java Benchmark of Recent Bugs Data and Tool Showcase Track André Silva KTH Royal Institute of Technology, Nuno Saavedra INESC-ID and IST, University of Lisbon, Martin Monperrus KTH Royal Institute of Technology | ||
11:56 4mTalk | A Dataset of Partial Program Fixes Data and Tool Showcase Track Dirk Beyer LMU Munich, Lars Grunske Humboldt-Universität zu Berlin, Matthias Kettl LMU Munich, Marian Lingsch-Rosenfeld LMU Munich, Moeketsi Raselimo Humboldt-Universität zu Berlin | ||
12:00 4mTalk | BugsPHP: A dataset for Automated Program Repair in PHP Data and Tool Showcase Track K.D. Pramod University of Moratuwa, Sri Lanka, W.T.N. De Silva University of Moratuwa, Sri Lanka, W.U.K. Thabrew University of Moratuwa, Sri Lanka, Ridwan Salihin Shariffdeen National University of Singapore, Sandareka Wickramanayake University of Moratuwa, Sri Lanka Pre-print | ||
12:04 4mTalk | AW4C: A Commit-Aware C Dataset for Actionable Warning Identification Data and Tool Showcase Track Zhipeng Liu , Meng Yan Chongqing University, Zhipeng Gao Shanghai Institute for Advanced Study - Zhejiang University, Dong Li , Xiaohong Zhang Chongqing University, Dan Yang Chongqing University | ||
12:08 5mTalk | Predicting the Impact of Crashes Across Release Channels Industry Track | ||
12:13 5mTalk | Zero Shot Learning based Alternatives for Class Imbalanced Learning Problem in Enterprise Software Defect Analysis Industry Track | ||