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MSR 2021
Mon 17 - Wed 19 May 2021
co-located with ICSE 2021
Mon 17 May 2021 17:24 - 17:27 at MSR Room 1 - Mining Challenge Session Chair(s): Miltiadis Allamanis, Rafael-Michael Karampatsis, Charles Sutton
Wed 19 May 2021 02:05 - 02:08 at MSR Room 1 - Bug Detection Chair(s): Raula Gaikovina Kula

Recent studies have shown the promising direction of deep learning based bug detection, which relieves human experts from the tedious and subjective task of manually summarizing features. Simple one-statement bugs (i.e., SStuBs), which occur relatively often in Java projects, cannot be well spotted by existing static analysis tools. In this paper, we make effort to empirically analyze whether deep learning based techniques could be used to detecting SStuBs. We have re-implemented two state-of-the-art techniques in approximately 3,000 lines of code and adopted them to detecting Java SStuBs. Experiments on large-scale datasets suggest that although deep learning based approaches can achieve much better results than existing static analyzers, the SStuBs cannot be well flagged when comparing with traditional complex vulnerabilities. We further look in detail on the per bug category basis, observing that deep learning based methods perform better when detecting some specific types of bugs (e.g., ``Same Function Change Caller''), which have strong data flow and control flow semantic. We believe our observations in this paper could offer implications on the automated detection and repair of SStuBs.

Mon 17 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

17:00 - 17:50
Mining Challenge SessionMining Challenge / Technical Papers at MSR Room 1
Chair(s): Miltiadis Allamanis Microsoft Research, UK, Rafael-Michael Karampatsis The University of Edinburgh, Charles Sutton Google Research
17:01
2m
Welcome by the Mining Challenge Co-chairs
Mining Challenge
Miltiadis Allamanis Microsoft Research, UK, Rafael-Michael Karampatsis The University of Edinburgh, Charles Sutton Google Research
17:03
3m
Talk
A large-scale study on human-cloned changes for automated program repair
Mining Challenge
Fernanda Madeiral KTH Royal Institute of Technology, Thomas Durieux KTH Royal Institute of Technology, Sweden
Link to publication Pre-print
17:06
3m
Talk
Applying CodeBERT for Automated Program Repair of Java Simple Bugs
Mining Challenge
Ehsan Mashhadi University of Calgary, Hadi Hemmati University of Calgary
Pre-print Media Attached
17:09
3m
Talk
PySStuBs: Characterizing Single-Statement Bugs in Popular Open-Source Python Projects
Mining Challenge
Arthur Veloso Kamienski University of Alberta, Luisa Palechor University of Alberta, Abram Hindle University of Alberta, Cor-Paul Bezemer University of Alberta
Pre-print
17:12
3m
Talk
How Effective is Continuous Integration in Indicating Single-Statement Bugs?
Mining Challenge
Jasmine Latendresse Concordia University, Rabe Abdalkareem Queens University, Kingston, Canada, Diego Costa Concordia University, Canada, Emad Shihab Concordia University
Pre-print
17:15
3m
Talk
Mea culpa: How developers fix their own simple bugs differently from other developers
Mining Challenge
Wenhan Zhu University of Waterloo, Michael W. Godfrey University of Waterloo, Canada
Pre-print
17:18
3m
Talk
On the Distribution of "Simple Stupid Bugs" in Unit Test Files: An Exploratory Study
Mining Challenge
Anthony Peruma Rochester Institute of Technology, Christian D. Newman Rochester Institute of Technology
Pre-print Media Attached
17:21
3m
Talk
On the Rise and Fall of Simple Stupid Bugs: a Life-Cycle Analysis of SStuBs
Mining Challenge
Balázs Mosolygó University of Szeged, Norbert Vándor University of Szeged, Gabor Antal University of Szeged, Peter Hegedus University of Szeged
Pre-print
17:24
3m
Talk
On the Effectiveness of Deep Vulnerability Detectors to Simple Stupid Bug Detection
Mining Challenge
Jiayi Hua Beijing University of Posts and Telecommunications, Haoyu Wang Beijing University of Posts and Telecommunications
Pre-print
17:27
23m
Live Q&A
Discussions and Q&A
Technical Papers

Wed 19 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

02:00 - 02:50
02:01
4m
Talk
Practitioners' Perceptions of the Goals and Visual Explanations of Defect Prediction Models
Technical Papers
Jirayus Jiarpakdee Monash University, Australia, Chakkrit Tantithamthavorn Monash University, John Grundy Monash University
Pre-print
02:05
3m
Talk
On the Effectiveness of Deep Vulnerability Detectors to Simple Stupid Bug Detection
Mining Challenge
Jiayi Hua Beijing University of Posts and Telecommunications, Haoyu Wang Beijing University of Posts and Telecommunications
Pre-print
02:08
4m
Talk
An Empirical Study of OSS-Fuzz Bugs
Technical Papers
Zhen Yu Ding Motional, Claire Le Goues Carnegie Mellon University
Pre-print
02:12
3m
Talk
Denchmark: A Bug Benchmark of Deep Learning-related Software
Data Showcase
Misoo Kim Sungkyunkwan University, Youngkyoung Kim Sungkyunkwan University, Eunseok Lee Sungkyunkwan University
02:15
4m
Talk
JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction
Technical Papers
Chanathip Pornprasit Monash University, Chakkrit Tantithamthavorn Monash University
Pre-print
02:19
31m
Live Q&A
Discussions and Q&A
Technical Papers


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