The DOI matches its Crossref record.
Code review quality: how developers see it
Abstract
In a large, long-lived project, an effective code review process is key to ensuring the long-term quality of the code base. In this work, we study code review practices of a large, open source project, and we investigate how the developers themselves perceive code review quality. We present a qualitative study that summarizes the results from a survey of 88 Mozilla core developers. The results provide developer insights into how they define review quality, what factors contribute to how they evaluate submitted code, and what challenges they face when performing review tasks. We found that the review quality is primarily associated with the thoroughness of the feedback, the reviewer's familiarity with the code, and the perceived quality of the code itself. Also, we found that while different factors are perceived to contribute to the review quality, reviewers often find it difficult to keep their technical skills up-to-date, manage personal priorities, and mitigate context switching.
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Most pull request datasets do not explicitly capture integration friction due to textual conflicts.
“As a result, integration friction due to textual conflicts is not explicitly captured in most pull request datasets.”
From AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub (2026) · cites this paper as [33]
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Cited by
Sorted by citations
Alami, A., & Ernst, N. (2025) · 2025 IEEE/ACM 18th International Conference on Cooperative and Human Aspects of Software Engineering (CHASE) · doi:10.1109/chase66643.2025.00016 · Cited by 15
AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub
Ogenrwot, D., & Businge, J. (2026) · Proceedings of the 3rd ACM International Conference on AI-Powered Software · doi:10.1145/3805760.3814923
“In addition, conflict behavior may vary across programming languages, repository sizes, and development practices.”
Who Pays the Review Cost? Triage, Fairness, and Accountability in AI-authored Pull Requests
Rahman, M. S., Alam, K., Roy, B., & Roy, C. K. (2026) · arXiv · doi:10.48550/arxiv.2610.11179
Plausible Is Not Enough: Trust and Evidence in AI-Authored Pull Request Review
Rahman, M. S., Alam, K., Roy, B., & Roy, C. K. (2026) · arXiv · doi:10.48550/arxiv.2610.21144
Metadata
- Authors
- Kononenko, O., Baysal, O., & Godfrey, M. W.
- Affiliations
- University of Waterloo (CA)
- Carleton University (CA)
- Venue
- Proceedings of the 38th International Conference on Software Engineering
- Publisher
- ACM
- Published
- 2016-05-14
- Pages
- 1028-1038
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2348249338
- Cited by
- 183
- References
- 0
- Citations by year
- 2026: 13 · 2025: 13 · 2024: 34 · 2023: 15 · 2022: 24 · 2021: 14 · 2020: 26 · 2019: 19 · 2018: 15 · 2017: 8 · 2016: 1
- FWCI
- 12.17
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Computer Science Applications › Open Source Software Innovations
- Keywords
- code review, review quality, developer perceptions, open source software, Mozilla, reviewer workload, context switching, large-scale software projects
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