SemBIC: Semantic-Aware Identification of Bug-Inducing Commits

Debugging can be much facilitated if one can identify the evolution commit that introduced the bug leading to a detected failure (aka. bug-inducing commit, BIC). Although one may, in theory, locate BICs by executing the detected failing test on various historical commit versions, it is impractical when the test cannot be executed on some of those versions. On the other hand, existing static techniques often assume the availability of additional information such as patches and bug reports, or the applicability of predefined heuristics like commit chronology. However, these approaches are ineffective when such assumptions do not hold, which are often the case in practice. To address these limitations, we propose SEMBIC to identify the BIC of a bug by statically tracking the semantic changes in the execution path prescribed by the failing test across successive historical commit versions. Our insight is that the greater the semantic changes a commit introduces concerning the failing execution path of a target bug, the more likely it is to be the BIC. To distill semantic changes relevant to the failure, we focus on three fine-grained semantic properties. We evaluate the performance of SEMBIC on a benchmark containing 199 real-world bugs from 12 open-source projects. We found that SEMBIC can identify BICs with high accuracy – it ranks the BIC as top 1 for 88 out of 199 bugs, and achieves an MRR of 0.520, outperforming the state-of-the-art technique by 29.4% and 13.6%, respectively.

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Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2025-06-19
DOI
https://doi.org/10.1145/3715781
Citations
1
Primary Topic
Software Engineering Research
Type
article
Field-Weighted Citation Impact
3.25
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article

SemBIC: Semantic-Aware Identification of Bug-Inducing Commits

Ming Wen, Hengcheng Zhu, Shing-Chi Cheung, Jialun Cao et al.
1 citations
Proceedings of the ACM on software engineering.
Software Engineering Research
3.25
article

SemBIC: Semantic-Aware Identification of Bug-Inducing Commits

Ming Wen, Hengcheng Zhu, Shing-Chi Cheung, Jialun Cao, Xiao Chen
article en
1 citations

Abstract

Debugging can be much facilitated if one can identify the evolution commit that introduced the bug leading to a detected failure (aka. bug-inducing commit, BIC). Although one may, in theory, locate BICs by executing the detected failing test on various historical commit versions, it is impractical when the test cannot be executed on some of those versions. On the other hand, existing static techniques often assume the availability of additional information such as patches and bug reports, or the applicability of predefined heuristics like commit chronology. However, these approaches are ineffective when such assumptions do not hold, which are often the case in practice. To address these limitations, we propose SEMBIC to identify the BIC of a bug by statically tracking the semantic changes in the execution path prescribed by the failing test across successive historical commit versions. Our insight is that the greater the semantic changes a commit introduces concerning the failing execution path of a target bug, the more likely it is to be the BIC. To distill semantic changes relevant to the failure, we focus on three fine-grained semantic properties. We evaluate the performance of SEMBIC on a benchmark containing 199 real-world bugs from 12 open-source projects. We found that SEMBIC can identify BICs with high accuracy – it ranks the BIC as top 1 for 88 out of 199 bugs, and achieves an MRR of 0.520, outperforming the state-of-the-art technique by 29.4% and 13.6%, respectively.

Proceedings of the ACM on software engineering.Vol. 2(FSE)
Hong Kong University of Science and Technology (HK), Huazhong University of Science and Technology (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Software Engineering Research
3.25
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SemBIC: Semantic-Aware Identification of Bug-Inducing Commits — Ming Wen, Hengcheng Zhu, et al. · Proceedings of the ACM on software engineering. (2025) | TGRS Research Map | TGRS