# AI-Assisted Assessment of Coding Practices in Modern Code Review

arXiv: https://arxiv.org/abs/2405.13565 · HTML: https://mlsanity.com/paper/2405.13565/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 4 of 4)

- [Scaling Up Models and Data with textttt5x and textttseqio](https://mlsanity.com/paper/2203.17189/) (2022)
- [Automating Code Review Activities by Large-Scale Pre-training](https://mlsanity.com/paper/2203.09095/) (2022)
- [Using Pre-Trained Models to Boost Code Review Automation](https://mlsanity.com/paper/2201.06850/) (2022)
- [Code Review Automation: Strengths and Weaknesses of the State of the Art](https://mlsanity.com/paper/2401.05136/) (2024)

## Built on by (most cited first, 16 of 16)

- [CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells](https://mlsanity.com/paper/2409.19801/) (2024)
- [RovoDev Code Reviewer: A Large-Scale Online Evaluation of LLM-based Code Review Automation at Atlassian](https://mlsanity.com/paper/2601.01129/) (2026)
- [Intuition to Evidence: Measuring AI's True Impact on Developer Productivity](https://mlsanity.com/paper/2509.19708/) (2025)
- [Understanding Dominant Themes in Reviewing Agentic AI-authored Code](https://mlsanity.com/paper/2601.19287/) (2026)
- [AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub](https://mlsanity.com/paper/2604.03551/) (2026)
- [Leveraging Reviewer Experience in Code Review Comment Generation](https://mlsanity.com/paper/2409.10959/) (2024)
- [Code Review Without Borders: Evaluating Synthetic vs. Real Data for Review Recommendation](https://mlsanity.com/paper/2509.04810/) (2025)
- [Understanding the Limits of Automated Evaluation for Code Review Bots in Practice](https://mlsanity.com/paper/2604.24525/) (2026)
- [Is Agentic Code Review Helpful? Mining Developers' Feedback to CodeRabbit Reviews in the Wild](https://mlsanity.com/paper/2607.03316/) (2026)
- [Beyond Code Generation: Reliability, Verification, and Cost Economics in the Agentic Software Development Lifecycle](https://mlsanity.com/paper/2609.04681/) (2026)
- [MetaLint: Easy-to-Hard Generalization for Code Linting](https://mlsanity.com/paper/2507.11687/) (2025)
- [Fine-Tuning Multilingual Language Models for Code Review: An Empirical Study on Industrial C# Projects](https://mlsanity.com/paper/2507.19271/) (2025)
- [A Note on Code Quality Score: LLMs for Maintainable Large Codebases](https://mlsanity.com/paper/2508.02732/) (2025)
- [ConceptRM: The Quest to Mitigate Alert Fatigue through Consensus-Based Purity-Driven Data Cleaning for Reflection Modelling](https://mlsanity.com/paper/2602.20166/) (2026)
- [From Code Review to Code Critique: Intent, Drift, and Spotlight for AI-Generated Diffs at Scale](https://mlsanity.com/paper/2607.29516/) (2026)
- [Characterizing the Quality Profile of AI-Generated C++ in Production](https://mlsanity.com/paper/2608.06640/) (2026)

## Abstract (arXiv)

Modern code review is a process in which an incremental code contribution made by a code author is reviewed by one or more peers before it is committed to the version control system. An important element of modern code review is verifying that code contributions adhere to best practices. While some of these best practices can be automatically verified, verifying others is commonly left to human reviewers. This paper reports on the development, deployment, and evaluation of AutoCommenter, a system backed by a large language model that automatically learns and enforces coding best practices. We implemented AutoCommenter for four programming languages (C++, Java, Python, and Go) and evaluated its performance and adoption in a large industrial setting. Our evaluation shows that an end-to-end system for learning and enforcing coding best practices is feasible and has a positive impact on the developer workflow. Additionally, this paper reports on the challenges associated with deploying such a system to tens of thousands of developers and the corresponding lessons learned.