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MSR 2021
Mon 17 - Wed 19 May 2021
co-located with ICSE 2021
Wed 19 May 2021 02:01 - 02:05 at MSR Room 1 - Bug Detection Chair(s): Raula Gaikovina Kula

Software defect prediction models are classifiers that are constructed from historical software data. Such software defect prediction models have been proposed to help developers optimize the limited Software Quality Assurance (SQA) resources and help managers develop SQA plans. Prior studies have different goals for their defect prediction models and use different techniques for generating visual explanations of their models. Yet, it is unclear what are the practitioners’ perceptions of (1) these defect prediction model goals, and (2) the model-agnostic techniques used to visualize these models. We conducted a qualitative survey to investigate practitioners’ perceptions of the goals of defect prediction models and the model-agnostic techniques used to generate visual explanations of defect prediction models. We found that (1) 82%-84% of the respondents perceived that the three goals of defect prediction models are useful; (2) LIME is the most preferred technique for understanding the most important characteristics that contributed to a prediction of a file, while ANOVA/VarImp is the second most preferred technique for understanding the characteristics that are associated with software defects in the past. Our findings highlight the significance of investigating how to improve the understanding of defect prediction models and their predictions. Hence, model-agnostic techniques from explainable AI domain may help practitioners to understand defect prediction models and their predictions.

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


Information for Participants
Wed 19 May 2021 02:00 - 02:50 at MSR Room 1 - Bug Detection Chair(s): Raula Gaikovina Kula
Info for room MSR Room 1:

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