Understanding Practitioners’ Expectations on Clear Code Review Comments

The code review comment (CRC) is pivotal in the process of modern code review. It provides reviewers with the opportunity to identify potential bugs, offer constructive feedback, and suggest improvements. Clear and concise code review comments (CRCs) facilitate the communication between developers and are crucial to the correct understanding of the identified issues and proposed solutions. Despite the importance of CRCs’ clarity, there is still a lack of guidelines on what constitutes a good clarity and how to evaluate it. In this paper, we conduct a comprehensive study on understanding and evaluating the clarity of CRCs. We first derive a set of attributes related to the clarity of CRCs, namely RIE attributes (i.e., Relevance , Informativeness , and Expression ), as well as their corresponding evaluation criteria based on our literature review and survey with practitioners. We then investigate the clarity of CRCs in open-source projects written in nine programming languages and find that a large portion (i.e., 28.8%) of the CRCs lack the clarity in at least one of the attributes. Finally, we explore the potential of automatically evaluating the clarity of CRCs by proposing ClearCRC. Experimental results show that ClearCRC with pre-trained language models is promising for effective evaluation of the clarity of CRCs, achieving a balanced accuracy up to 73.04% and a F-1 score up to 94.61%.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2025-06-22
DOI
https://doi.org/10.1145/3728931
Citations
3
Primary Topic
Software Engineering Research
Type
article
Field-Weighted Citation Impact
9.74
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Understanding Practitioners’ Expectations on Clear Code Review Comments

Zhenhao Li, Xin Xia, Xing Hu, Qiheng Mao et al.
3 citations
Proceedings of the ACM on software engineering.
Software Engineering Research
9.74
article

Understanding Practitioners’ Expectations on Clear Code Review Comments

Zhenhao Li, Xin Xia, Xing Hu, Qiheng Mao, Kui Liu, Junkai Chen
article en
3 citations

Abstract

The code review comment (CRC) is pivotal in the process of modern code review. It provides reviewers with the opportunity to identify potential bugs, offer constructive feedback, and suggest improvements. Clear and concise code review comments (CRCs) facilitate the communication between developers and are crucial to the correct understanding of the identified issues and proposed solutions. Despite the importance of CRCs’ clarity, there is still a lack of guidelines on what constitutes a good clarity and how to evaluate it. In this paper, we conduct a comprehensive study on understanding and evaluating the clarity of CRCs. We first derive a set of attributes related to the clarity of CRCs, namely RIE attributes (i.e., Relevance , Informativeness , and Expression ), as well as their corresponding evaluation criteria based on our literature review and survey with practitioners. We then investigate the clarity of CRCs in open-source projects written in nine programming languages and find that a large portion (i.e., 28.8%) of the CRCs lack the clarity in at least one of the attributes. Finally, we explore the potential of automatically evaluating the clarity of CRCs by proposing ClearCRC. Experimental results show that ClearCRC with pre-trained language models is promising for effective evaluation of the clarity of CRCs, achieving a balanced accuracy up to 73.04% and a F-1 score up to 94.61%.

Proceedings of the ACM on software engineering.Vol. 2(ISSTA)
York University (CA), Zhejiang University (CN)
Openalex Percentile: Top 2%
Software Engineering Research
9.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Understanding Practitioners’ Expectations on Clear Code Review Comments — Zhenhao Li, Xin Xia, et al. · Proceedings of the ACM on software engineering. (2025) | TGRS Research Map | TGRS