research-article

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

Authors:

Oscar Svensson,

Daniel HanssonAuthors Info & Claims

MaLTeSQuE 2019: Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation

Pages 7 - 12

https://doi.org/10.1145/3340482.3342742

Published: 27 August 2019 Publication History

Abstract

Machine learning applications in software engineering often 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.

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Cited By

Jiang MJiang JWu TMa ZLuo XZhou Y(2024)Understanding Vulnerability Inducing Commits of the Linux KernelACM Transactions on Software Engineering and Methodology10.1145/367245233:7(1-28)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3672452
Olewicki DHabchi SAdams B(2024)An Empirical Study on Code Review Activity Prediction and Its Impact in PracticeProceedings of the ACM on Software Engineering10.1145/36608061:FSE(2238-2260)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660806
Lyu YKang HWidyasari RLawall JLo D(2024)Evaluating SZZ Implementations: An Empirical Study on the Linux KernelIEEE Transactions on Software Engineering10.1109/TSE.2024.340671850:9(2219-2239)Online publication date: Sep-2024
https://doi.org/10.1109/TSE.2024.3406718
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Index Terms

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
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Maintaining software
  2. Software notations and tools
    1. Software configuration management and version control systems
    2. Software maintenance tools

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cover image ACM Conferences

MaLTeSQuE 2019: Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation

August 2019

42 pages

ISBN:9781450368551

DOI:10.1145/3340482

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 27 August 2019

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ITEA3

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ESEC/FSE '19

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ESEC/FSE '19: 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 27, 2019

Tallinn, Estonia

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Cited By

Jiang MJiang JWu TMa ZLuo XZhou Y(2024)Understanding Vulnerability Inducing Commits of the Linux KernelACM Transactions on Software Engineering and Methodology10.1145/367245233:7(1-28)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3672452
Olewicki DHabchi SAdams B(2024)An Empirical Study on Code Review Activity Prediction and Its Impact in PracticeProceedings of the ACM on Software Engineering10.1145/36608061:FSE(2238-2260)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660806
Lyu YKang HWidyasari RLawall JLo D(2024)Evaluating SZZ Implementations: An Empirical Study on the Linux KernelIEEE Transactions on Software Engineering10.1109/TSE.2024.340671850:9(2219-2239)Online publication date: Sep-2024
https://doi.org/10.1109/TSE.2024.3406718
Watanabe HKondo MChoi EMizuno O(2024)Benefits and Pitfalls of Token-Level SZZ: An Empirical Study on OSS Projects2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER60148.2024.00084(776-786)Online publication date: 12-Mar-2024
https://doi.org/10.1109/SANER60148.2024.00084
Zhao YLi XDeng WLi YGuo XTian QFan Y(2024)Fine-Grained Bug Localization Based on Rich Context using Attention Tree-GRU2024 5th International Conference on Computer Engineering and Application (ICCEA)10.1109/ICCEA62105.2024.10603974(640-646)Online publication date: 12-Apr-2024
https://doi.org/10.1109/ICCEA62105.2024.10603974
Suhag VDubey SSharma B(2024)Software defect prediction using global and local modelsInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02407-715:8(4003-4017)Online publication date: 2-Jul-2024
https://doi.org/10.1007/s13198-024-02407-7
Maes-Bermejo MSerebrenik AGallego MGortázar FRobles GGonzález Barahona J(2024)Hunting bugs: Towards an automated approach to identifying which change caused a bug through regression testingEmpirical Software Engineering10.1007/s10664-024-10479-z29:3Online publication date: 4-May-2024
https://doi.org/10.1007/s10664-024-10479-z
Bryan JMoriano P(2023)Graph-based machine learning improves just-in-time defect predictionPLOS ONE10.1371/journal.pone.028407718:4(e0284077)Online publication date: 13-Apr-2023
https://doi.org/10.1371/journal.pone.0284077
Reid DRahkema KWalden JMcIntosh SChoi EHerbold S(2023)Large Scale Study of Orphan Vulnerabilities in the Software Supply ChainProceedings of the 19th International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3617555.3617872(22-32)Online publication date: 8-Dec-2023
https://dl.acm.org/doi/10.1145/3617555.3617872
Liu SGuo ZLi YWang CChen LSun ZZhou YXu B(2023)Inconsistent Defect Labels: Essence, Causes, and InfluenceIEEE Transactions on Software Engineering10.1109/TSE.2022.315678749:2(586-610)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TSE.2022.3156787
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