The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
The impact of code review coverage and code review participation on software quality: a case study of the qt, VTK, and ITK projects
Abstract
Software code review, i.e., the practice of having third-party team members critique changes to a software system, is a well-established best practice in both open source and proprietary software domains. Prior work has shown that the formal code inspections of the past tend to improve the quality of software delivered by students and small teams. However, the formal code inspection process mandates strict review criteria (e.g., in-person meetings and reviewer checklists) to ensure a base level of review quality, while the modern, lightweight code reviewing process does not. Although recent work explores the modern code review process qualitatively, little research quantitatively explores the relationship between properties of the modern code review process and software quality. Hence, in this paper, we study the relationship between software quality and: (1) code review coverage, i.e., the proportion of changes that have been code reviewed, and (2) code review participation, i.e., the degree of reviewer involvement in the code review process. Through a case study of the Qt, VTK, and ITK projects, we find that both code review coverage and participation share a significant link with software quality. Low code review coverage and participation are estimated to produce components with up to two and five additional post-release defects respectively. Our results empirically confirm the intuition that poorly reviewed code has a negative impact on software quality in large systems using modern reviewing tools.
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Use of relative code churn measures to predict system defect density
Nagappan, N., & Ball, T. (2005) · Proceedings of the 27th international conference on Software engineering - ICSE '05 · doi:10.1145/1062455.1062514 · Cited by 775
(2013) · doi:10.5555/2486788.2486882 · Cited by 437
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An empirical study of the impact of modern code review practices on software quality
McIntosh, S., Kamei, Y., Adams, B., & Hassan, A. E. (2016) · Empirical Software Engineering · doi:10.1007/s10664-015-9381-9 · Cited by 332
Self-citationAn Empirical Study of Model-Agnostic Techniques for Defect Prediction Models
Jiarpakdee, J., Tantithamthavorn, C. K., Dam, H. K., & Grundy, J. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.2982385 · Cited by 148
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Code Review Automation: Strengths and Weaknesses of the State of the Art
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Metadata
- Authors
- McIntosh, S., Kamei, Y., Adams, B., & Hassan, A. E.
- Venue
- Proceedings of the 11th Working Conference on Mining Software Repositories
- Publisher
- ACM
- Published
- 2014-05-31
- Pages
- 192-201
- OpenAlex
- W2108769867
- Cited by
- 348
- References
- 3
- FWCI
- 29.64
- Type
- conference paper
- Language
- en
- Open access
- closed
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- McIntosh, S., Kamei, Y., Adams, B., & Hassan, A. E. (2014). The impact of code review coverage and code review participation on software quality: a case study of the qt, VTK, and ITK projects. Proceedings of the 11th Working Conference on Mining Software Repositories. https://doi.org/10.1145/2597073.2597076
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