AI-Assisted Assessment of Coding Practices in Modern Code Review
This addresses the problem of automating coding practice verification in code review for developers, though it is incremental as it builds on existing AI methods for a known bottleneck.
The paper developed AutoCommenter, an AI system using a large language model to automatically learn and enforce coding best practices in code review for four programming languages, showing feasibility and positive impact on developer workflow in an industrial setting.
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.