Evaluating SZZ Implementations: An Empirical Study on the Linux Kernel
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Abstract (arXiv)
The SZZ algorithm is used to connect bug-fixing commits to the earlier commits that introduced bugs. This algorithm has many applications and many variants have been devised. However, there are some types of commits that cannot be traced by the SZZ algorithm, referred to as "ghost commits". The evaluation of how these ghost commits impact the SZZ algorithm remains limited. Moreover, these algorithms have been evaluated on datasets created by software engineering researchers from information in bug trackers and version controlled histories. Since Oct 2013, the Linux kernel developers have started labelling bug-fixing patches with the commit identifiers of the corresponding bug-inducing commit(s) as a standard practice. As of v6.1-rc5, 76,046 pairs of bug-fixing patches and bug-inducing commits are available. This provides a unique opportunity to evaluate the SZZ algorithm on a large dataset that has been created and reviewed by project developers, entirely independently of the biases of software engineering researchers. In this paper, we apply six SZZ algorithms to 76,046 pairs of bug-fixing patches and bug-introducing commits from the Linux kernel. Our findings reveal that SZZ algorithms experience a more significant decline in recall on our dataset (13.8%) as compared to prior findings reported by Rosa et al., and the disparities between the individual SZZ algorithms diminish. Moreover, we find that 17.47% of bug-fixing commits are ghost commits. Finally, we propose Tracing-Commit SZZ (TC-SZZ), that traces all commits in the change history of lines modified or deleted in bug-fixing commits. Applying TC-SZZ to all failure cases, excluding ghost commits, we found that TC-SZZ could identify 17.7% of them. Our further analysis found that 34.6% of bug-inducing commits were in the function history, 27.5% in the file history (but not in the function history), and...
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Aug 2023.
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Citing papers per year: 2025: 4; 2026: 3.
7 papers.
- 2025SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarioscited by 11
- 2025Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models?cited by 4
- 2025"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistantscited by 4
- 2026AgenticSZZ: Temporal Knowledge Graph-Guided Agentic Bug-Inducing Commit Identificationcited by 1
- 2026How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Studycited by 1
- 2025ReDef: Do Code Language Models Truly Understand Code Changes for Just-in-Time Software Defect Prediction?cited by 0
- 2026TraceVIC: Causal Reasoning over Code Evolution for Identifying Vulnerability-Inducing Commitscited by 0
- 2026TraceVIC: Causal Reasoning over Code Evolution for Identifying Vulnerability-Inducing Commits22 Sep 2026
- 2026How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study12 Jul 2026
- 2026AgenticSZZ: Temporal Knowledge Graph-Guided Agentic Bug-Inducing Commit Identification3 Feb 2026
- 2025SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios26 Sep 2025
- 2025ReDef: Do Code Language Models Truly Understand Code Changes for Just-in-Time Software Defect Prediction?11 Sep 2025
- 2025"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistants17 Aug 2025
- 2025Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models?27 Jan 2025