SZZ Unleashed: An Open Implementation of the SZZ Algorithm -- Featuring Example Usage in a Study of Just-in-Time Bug Prediction for the Jenkins Project
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Abstract (arXiv)
Numerous empirical software engineering studies rely on detailed information about bugs. While issue trackers often contain information about when bugs were fixed, details about when they were introduced to the system are often absent. As a remedy, researchers often rely on the SZZ algorithm as a heuristic approach to identify bug-introducing software changes. Unfortunately, as reported in a recent systematic literature review, few researchers have made their SZZ implementations publicly available. Consequently, there is a risk that research effort is wasted as new projects based on SZZ output need to initially reimplement the approach. Furthermore, there is a risk that newly developed (closed source) SZZ implementations have not been properly tested, thus conducting research based on their output might introduce threats to validity. We present SZZ Unleashed, an open implementation of the SZZ algorithm for git repositories. This paper describes our implementation along with a usage example for the Jenkins project, and conclude with an illustrative study on just-in-time bug prediction. We hope to continue evolving SZZ Unleashed on GitHub, and warmly invite the community to contribute.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Mar 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
5 papers.
- 2021Graph-Based Machine Learning Improves Just-in-Time Defect Predictioncited by 2
- 2022Leveraging Structural Properties of Source Code Graphs for Just-In-Time Bug Predictioncited by 2
- 2025Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Studycited by 1
- 2023A comprehensible analysis of the efficacy of Ensemble Models for Bug Predictioncited 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
- 2025Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study8 Jan 2025
- 2023A comprehensible analysis of the efficacy of Ensemble Models for Bug Prediction18 Oct 2023
- 2022Leveraging Structural Properties of Source Code Graphs for Just-In-Time Bug Prediction25 Jan 2022
- 2021Graph-Based Machine Learning Improves Just-in-Time Defect Prediction11 Oct 2021