A Systematic Literature Review and Taxonomy of Modern Code Review
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Abstract (arXiv)
Modern Code Review (MCR) is a widely known practice of software quality assurance. However, the existing body of knowledge of MCR is currently not understood as a whole. Objective: Our goal is to identify the state of the art on MCR, providing a structured overview and an in-depth analysis of the research done in this field. Method: We performed a systematic literature review, selecting publications from four digital libraries. Results: A total of 139 papers were selected and analyzed in three main categories. Foundational studies are those that analyze existing or collected data from the adoption of MCR. Proposals consist of techniques and tools to support MCR, while evaluations are studies to assess an approach or compare a set of them. Conclusion: The most represented category is foundational studies, mainly aiming to understand the motivations for adopting MCR, its challenges and benefits, and which influence factors lead to which MCR outcomes. The most common types of proposals are code reviewer recommender and support to code checking. Evaluations of MCR-supporting approaches have been done mostly offline, without involving human subjects. Five main research gaps have been identified, which point out directions for future work in the area.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Mar 2021.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 2; 2026: 2.
7 papers.
- 2024CodeAgent: Autonomous Communicative Agents for Code Reviewcited by 33
- 2025Evaluating Large Language Models for Code Reviewcited by 7
- 2026HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automationcited by 5
- 2023Predicting the First Response Latency of Maintainers and Contributors in Pull Requestscited by 3
- 2025Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvementscited by 2
- 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Reviewcited by 1
- 2023Team-related Features in Code Review Prediction Modelscited by 0
- 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review17 May 2026
- 2026HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation27 Jan 2026
- 2025Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements12 Jun 2025
- 2025Evaluating Large Language Models for Code Review26 May 2025
- 2024CodeAgent: Autonomous Communicative Agents for Code Review3 Feb 2024
- 2023Team-related Features in Code Review Prediction Models11 Dec 2023
- 2023Predicting the First Response Latency of Maintainers and Contributors in Pull Requests13 Nov 2023