Exploring and exploiting the correlations between bug-inducing and bug-fixing commits

Wen，Ming1; Wu，Rongxin2; Liu，Yepang3; Tian，Yongqiang4; Xie，Xuan5; Cheung，Shing Chi4; Su，Zhendong6

10.1145/3338906.3338962

2019-08-12

ESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering

326-337

Tallinn, Estonia

Association for Computing Machinery, Inc

Bug-inducing commits provide important information to understand when and how bugs were introduced. Therefore, they have been extensively investigated by existing studies and frequently leveraged to facilitate bug fixings in industrial practices. Due to the importance of bug-inducing commits in software debugging, we are motivated to conduct the first systematic empirical study to explore the correlations between bug-inducing and bug-fixing commits in terms of code elements and modifications. To facilitate the study, we collected the inducing and fixing commits for 333 bugs from seven large open-source projects. The empirical findings reveal important and significant correlations between a bug's inducing and fixing commits. We further exploit the usefulness of such correlation findings from two aspects. First, they explain why the SZZ algorithm, the most widely-adopted approach to collecting bug-inducing commits, is imprecise. In view of SZZ's imprecision, we revisited the findings of previous studies based on SZZ, and found that 8 out of 10 previous findings are significantly affected by SZZ's imprecision. Second, they shed lights on the design of automated debugging techniques. For demonstration, we designed approaches that exploit the correlations with respect to statements and change actions. Our experiments on Defects4J show that our approaches can boost the performance of fault localization significantly and also advance existing APR techniques.

Bug-inducing commits Empirical study Fault localization and repair

非南科大

英语

EI ; CPCI-S

WOS:000485629300032

20193707420987

Open source software

Computer Software, Data Handling and Applications:723 ; Computer Programming:723.1

2-s2.0-85071928851

Scopus

1.Hong Kong University of Science and Technology (HKUST),Hong Kong,Hong Kong
2.Department of Cyber Space Security,Xiamen University,Xiamen,China
3.Shenzhen Key Laboratory of Computational Intelligence,SUSTech,Shenzhen,China
4.HKUST,Hong Kong,Hong Kong
5.Sun Yat-sen University,China
6.ETH Zurich,Switzerland

Wen，Ming,Wu，Rongxin,Liu，Yepang,et al. Exploring and exploiting the correlations between bug-inducing and bug-fixing commits[C]:Association for Computing Machinery, Inc,2019:326-337.