Improving LLM-Based Go Code Review

Improving LLM-Based Go Code Review through Issue-List Generation and Context Augmentation

Announce Type: new Abstract: LLMs have shown strong potential for automating code review, yet their practical utility depends heavily on the design of generation and context strategies. In this paper, we investigate how to improve LLM-based code review through generation strategy and contextual augmentation. We first propose an issue-list review paradigm, in which LLMs enumerate all potential issues rather than reporting only the single most important one (i.e., primary-issue review).