Code Review Comprehension: Reviewing Strategies Seen Through Code Comprehension Theories
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Abstract (arXiv)
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 theory-driven 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
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Mar 2025.
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Citing papers per year: 2026: 4.
4 papers.
- 2026AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHubcited by 5
- 2026AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detectioncited by 2
- 2026Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networkscited by 0
- 2026CRJudgeBench: Can AI Detect Plausible but Invalid Code Reviews?cited by 0
- 2026CRJudgeBench: Can AI Detect Plausible but Invalid Code Reviews?29 Sep 2026
- 2026Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networks1 Jun 2026
- 2026AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub4 Apr 2026
- 2026AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection27 Jan 2026