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MSR 2021
Mon 17 - Wed 19 May 2021
co-located with ICSE 2021
Wed 19 May 2021 02:15 - 02:19 at MSR Room 1 - Bug Detection Chair(s): Raula Gaikovina Kula

A Just-In-Time (JIT) defect prediction model is a classifier to predict if a commit is defect-introducing. Recently, CC2Vec – a deep learning approach for Just-In-Time defect prediction – has been proposed. However, CC2Vec requires the whole dataset (i.e., training + testing) for model training, assuming that all unlabelled testing datasets would be available beforehand, which does not follow the key principles of just-in-time defect predictions. Our replication study shows that, after excluding the testing dataset for model training, the F-measure of CC2Vec is decreased by 38.5% for OpenStack and 45.7% for Qt, highlighting the negative impact of excluding the testing dataset for Just-In-Time defect prediction. In addition, CC2Vec cannot perform fine-grained predictions at the line level (i.e., which lines are most risky for a given commit).

In this paper, we propose JITLine – a Just-In-Time defect prediction approach for predicting defect-introducing commits and identifying lines that are associated with that defect-introducing commit (i.e., defective lines). Through a case study of 37,524 commits from OpenStack and Qt, we find that our JITLine approach is at least 26%-38% more accurate (F-measure), 17%-51% more cost-effective (PCI@20%LOC), 70-100 times faster than the state-of-the-art approaches (i.e., CC2Vec and DeepJIT) and the fine-grained predictions at the line level by our approach are 133%-150% more accurate (Top-10 Accuracy) than the baseline NLP approach. Therefore, our JITLine approach may help practitioners to better prioritize defect-introducing commits and better identify defective lines.

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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