AI-Assisted Assessment of Coding Practices in Modern Code Review

TL;DR

This paper introduces AutoCommenter, a system using large language models to automatically learn and enforce coding best practices during code reviews, demonstrating its feasibility and positive impact in an industrial setting.

Contribution

It presents the development, deployment, and evaluation of AutoCommenter, a novel LLM-based system for automating coding best practice enforcement across multiple programming languages.

Findings

AutoCommenter effectively learns coding best practices.

Deployment improved developer workflow.

System is feasible at large scale.

Abstract

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.