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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.

Conference Day
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 JiarpakdeeMonash University, Australia, Chakkrit TantithamthavornMonash University, John GrundyMonash University
Pre-print
02:05
3m
Talk
On the Effectiveness of Deep Vulnerability Detectors to Simple Stupid Bug Detection
Mining Challenge
Jiayi HuaBeijing University of Posts and Telecommunications, Haoyu WangBeijing University of Posts and Telecommunications
Pre-print
02:08
4m
Talk
An Empirical Study of OSS-Fuzz Bugs
Technical Papers
Zhen Yu DingMotional, Claire Le GouesCarnegie Mellon University
Pre-print
02:12
3m
Talk
Denchmark: A Bug Benchmark of Deep Learning-related Software
Data Showcase
Misoo KimSungkyunkwan University, Youngkyoung KimSungkyunkwan University, Eunseok LeeSungkyunkwan University
02:15
4m
Talk
JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction
Technical Papers
Chanathip PornprasitMonash University, Chakkrit TantithamthavornMonash University
Pre-print
02:19
31m
Live Q&A
Discussions and Q&A
Technical Papers


Information for Participants
Info for MSR Room 1: