LLMs can reliably classify code changes into structured categories (renames, moves, logic changes, etc.) to automate and prioritize code review tasks, achieving strong accuracy while being language-agnostic and customizable.
This paper shows how large language models can automatically label and categorize code changes in patches (like identifying renames, moves, or logic modifications) to make code review faster and more efficient. Using a two-stage approach with few-shot prompting, the method achieves 84% recall and 81% precision without needing traditional static analysis tools.