What Types of Code Review Comments Do Developers Most Frequently Resolve?
Authors
Saul Goldman
Hong Yi Lin
Jirat Pasuksmit
Patanamon Thongtanunam
Kla Tantithamthavorn
Zhe Wang
Ray Zhang
Ali Behnaz
Fan Jiang
Michael Siers
Ryan Jiang
Mike Buller
Minwoo Jeong
Ming Wu
Abstract
Large language model (LLM)-powered code review automation tools have been introduced to generate code review comments. However, not all generated comments will drive code changes. Understanding what types of generated review comments are likely to trigger code changes is crucial for identifying those that are actionable. In this paper, we set out to investigate (1) the types of review comments written by humans and LLMs, and (2) the types of generated comments that are most frequently resolved by developers. To do so, we developed an LLM-as-a-Judge to automatically classify review comments based on our own taxonomy of five categories. Our empirical study confirms that (1) the LLM reviewer and human reviewers exhibit distinct strengths and weaknesses depending on the project context, and (2) readability, bugs, and maintainability-related comments had higher resolution rates than those focused on code design. These results suggest that a substantial proportion of LLM-generated comments are actionable and can be resolved by developers. Our work highlights the complementarity between LLM and human reviewers and offers suggestions to improve the practical effectiveness of LLM-powered code review tools.
