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Code Review Comprehension: Reviewing Strategies Seen Through Code Comprehension Theories
Open accessAbstract
Despite the popularity and importance of modern code review, the understanding of the cognitive processes that enable reviewers to analyze code and provide meaningful feedback is lacking. To address this gap, we observed and interviewed ten experienced reviewers while they performed 25 code reviews from their review queue. Since comprehending code changes is essential to perform code review and the primary challenge for reviewers, we focused our analysis on this cognitive process. Using Letovsky's model of code comprehension, we performed a theorydriven thematic analysis to investigate how reviewers apply code comprehension to navigate changes and provide feedback. Our findings confirm that code comprehension is fundamental to code review. We extend Letovsky's model to propose the Code Review Comprehension Model and demonstrate that code review, like code comprehension, relies on opportunistic strategies. These strategies typically begin with a context-building phase, followed by code inspection involving code reading, testing, and discussion management. To interpret and evaluate the proposed change, reviewers construct a mental model of the change as an extension of their understanding of the overall software system and contrast mental representations of expected and ideal solutions against the actual implementation. Based on our findings, we discuss how review tools and practices can better support reviewers in employing their strategies and in forming understanding. Data and material: https://doi.org/10.5281/zenodo.14748996
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“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 [24]
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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.”
Metadata
- Authors
- Gonçalves, P. W., Rani, P., Storey, M.-A., Spinellis, D., & Bacchelli, A.
- Affiliations
- University of Zurich (CH)
- University of Victoria (CA)
- Athens University of Economics and Business (GR)
- Venue
- 2025 IEEE/ACM 33rd International Conference on Program Comprehension (ICPC)
- Publisher
- IEEE
- Published
- 2025-04-27
- Pages
- 589-601
- OpenAlex
- W4411360040
- Cited by
- 4
- References
- 3
- Citations by year
- 2026: 4
- FWCI
- 4.79
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Program Comprehension (ICPC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Swiss National Science Foundation (200021_197227,200021M_205146)
- 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, program comprehension, opportunistic strategy, mental models, software inspection, review tools
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