# Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation

arXiv: https://arxiv.org/abs/2502.02757 · HTML: https://mlsanity.com/paper/2502.02757/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2025.

## Built on by (most cited first, 12 of 12)

- [RovoDev Code Reviewer: A Large-Scale Online Evaluation of LLM-based Code Review Automation at Atlassian](https://mlsanity.com/paper/2601.01129/) (2026)
- [HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation](https://mlsanity.com/paper/2601.19072/) (2026)
- [AACR-Bench: Evaluating Automatic Code Review with Holistic Repository-Level Context](https://mlsanity.com/paper/2601.19494/) (2026)
- [Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics](https://mlsanity.com/paper/2508.08661/) (2025)
- [CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models](https://mlsanity.com/paper/2503.16167/) (2025)
- [Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review](https://mlsanity.com/paper/2605.17548/) (2026)
- [Is Agentic Code Review Helpful? Mining Developers' Feedback to CodeRabbit Reviews in the Wild](https://mlsanity.com/paper/2607.03316/) (2026)
- [Agentic Code Review in the Terminal: A Trajectory-Level Analysis of Behavior, Cost, and Human-Alignment](https://mlsanity.com/paper/2607.16740/) (2026)
- [On The Impact of Merge Request Deviations on Code Review Practices](https://mlsanity.com/paper/2506.08860/) (2025)
- [Enhancing Neural Code Representation with Additional Context](https://mlsanity.com/paper/2510.12082/) (2025)
- [Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision](https://mlsanity.com/paper/2604.06723/) (2026)
- [CRJudgeBench: Can AI Detect Plausible but Invalid Code Reviews?](https://mlsanity.com/paper/2609.37216/) (2026)

## Abstract (arXiv)

Code review is an important practice in software development, yet it is time-consuming and requires substantial effort. While open-source datasets have been used to train neural models for automating code review tasks, including review comment generation, these datasets contain a significant amount of noisy comments (e.g., vague or non-actionable feedback) that persist despite cleaning methods using heuristics and machine learning approaches. Such remaining noise may lead models to generate low-quality review comments, yet removing them requires a complex semantic understanding of both code changes and natural language comments. In this paper, we investigate the impact of such noise on review comment generation and propose a novel approach using large language models (LLMs) to further clean these datasets. Based on an empirical study on a large-scale code review dataset, our LLM-based approach achieves 66-85% precision in detecting valid comments. Using the predicted valid comments to fine-tune the state-of-the-art code review models (cleaned models) can generate review comments that are 13.0% - 12.4% more similar to valid human-written comments than the original models. We also find that the cleaned models can generate more informative and relevant comments than the original models. Our findings underscore the critical impact of dataset quality on the performance of review comment generation. We advocate for further research into cleaning training data to enhance the practical utility and quality of automated code review.