@inproceedings{naik-etal-2025-crscore,
title = "{CRS}core: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells",
author = "Naik, Atharva and
Alenius, Marcus and
Fried, Daniel and
Rose, Carolyn",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.457/",
doi = "10.18653/v1/2025.naacl-long.457",
pages = "9049--9076",
ISBN = "979-8-89176-189-6",
abstract = "The task of automated code review has recently gained a lot of attention from the machine learning community. However, current review comment evaluation metrics rely on comparisons with a human-written reference for a given code change (also called a diff ). Furthermore, code review is a one-to-many problem, like generation and summarization, with many ``valid reviews'' for a diff. Thus, we develop CRScore {---} a reference-free metric to measure dimensions of review quality like conciseness, comprehensiveness, and relevance. We design CRScore to evaluate reviews in a way that is grounded in claims and potential issues detected in the code by LLMs and static analyzers. We demonstrate that CRScore can produce valid, fine-grained scores of review quality that have the greatest alignment with human judgment among open-source metrics (0.54 Spearman correlation) and are more sensitive than reference-based metrics. We also release a corpus of 2.9k human-annotated review quality scores for machine-generated and GitHub review comments to support the development of automated metrics."
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<abstract>The task of automated code review has recently gained a lot of attention from the machine learning community. However, current review comment evaluation metrics rely on comparisons with a human-written reference for a given code change (also called a diff ). Furthermore, code review is a one-to-many problem, like generation and summarization, with many “valid reviews” for a diff. Thus, we develop CRScore — a reference-free metric to measure dimensions of review quality like conciseness, comprehensiveness, and relevance. We design CRScore to evaluate reviews in a way that is grounded in claims and potential issues detected in the code by LLMs and static analyzers. We demonstrate that CRScore can produce valid, fine-grained scores of review quality that have the greatest alignment with human judgment among open-source metrics (0.54 Spearman correlation) and are more sensitive than reference-based metrics. We also release a corpus of 2.9k human-annotated review quality scores for machine-generated and GitHub review comments to support the development of automated metrics.</abstract>
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%0 Conference Proceedings
%T CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells
%A Naik, Atharva
%A Alenius, Marcus
%A Fried, Daniel
%A Rose, Carolyn
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F naik-etal-2025-crscore
%X The task of automated code review has recently gained a lot of attention from the machine learning community. However, current review comment evaluation metrics rely on comparisons with a human-written reference for a given code change (also called a diff ). Furthermore, code review is a one-to-many problem, like generation and summarization, with many “valid reviews” for a diff. Thus, we develop CRScore — a reference-free metric to measure dimensions of review quality like conciseness, comprehensiveness, and relevance. We design CRScore to evaluate reviews in a way that is grounded in claims and potential issues detected in the code by LLMs and static analyzers. We demonstrate that CRScore can produce valid, fine-grained scores of review quality that have the greatest alignment with human judgment among open-source metrics (0.54 Spearman correlation) and are more sensitive than reference-based metrics. We also release a corpus of 2.9k human-annotated review quality scores for machine-generated and GitHub review comments to support the development of automated metrics.
%R 10.18653/v1/2025.naacl-long.457
%U https://aclanthology.org/2025.naacl-long.457/
%U https://doi.org/10.18653/v1/2025.naacl-long.457
%P 9049-9076
Markdown (Informal)
[CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells](https://aclanthology.org/2025.naacl-long.457/) (Naik et al., NAACL 2025)
ACL