Modern Code Reviews -- Survey of Literature and Practice
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Abstract (arXiv)
Background: Modern Code Review (MCR) is a lightweight alternative to traditional code inspections. While secondary studies on MCR exist, it is unknown whether the research community has targeted themes that practitioners consider important. Objectives: The objectives are to provide an overview of MCR research, analyze the practitioners' opinions on the importance of MCR research, investigate the alignment between research and practice, and propose future MCR research avenues. Method: We conducted a systematic mapping study to survey state of the art until and including 2021, employed the Q-Methodology to analyze the practitioners' perception of the relevance of MCR research, and analyzed the primary studies' research impact. Results: We analyzed 244 primary studies, resulting in five themes. As a result of the 1,300 survey data points, we found that the respondents are positive about research investigating the impact of MCR on product quality and MCR process properties. In contrast, they are negative about human factor- and support systems-related research. Conclusion: These results indicate a misalignment between the state of the art and the themes deemed important by most survey respondents. Researchers should focus on solutions that can improve the state of MCR practice. We provide an MCR research agenda that can potentially increase the impact of MCR research.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Feb 2024.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 3.
6 papers.
- 2024CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smellscited by 8
- 2025Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practicecited by 4
- 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Reviewcited by 1
- 2025Level Up Peer Review in Education: Investigating genAI-driven Gamification system and its influence on Peer Feedback Effectivenesscited by 0
- 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requestscited by 0
- 2026Using Agentic AI for contextualized and multifaceted code review at Ericssoncited by 0
- 2026Using Agentic AI for contextualized and multifaceted code review at Ericsson14 Sep 2026
- 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests23 Jul 2026
- 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review17 May 2026
- 2025Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practice10 Nov 2025
- 2025Level Up Peer Review in Education: Investigating genAI-driven Gamification system and its influence on Peer Feedback Effectiveness3 Apr 2025
- 2024CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells29 Sep 2024