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Automatic program repair (APR) techniques have the potential to reduce manualefforts in uncovering and repairing program defects during the code review (CR)process. However, the limited accuracy and considerable time costs associatedwith existing APR approaches hinder their adoption in industrial practice. Onekey factor is the under-utilization of review comments, which provide valuableinsights into defects and potential fixes. Recent advancements in LargeLanguage Models (LLMs) have enhanced their ability to comprehend natural andprogramming languages, enabling them to generate patches based on reviewcomments. This paper conducts a comprehensive investigation into the effectiveutilization of LLMs for repairing CR defects. In this study, various promptsare designed and compared across mainstream LLMs using two distinct datasetsfrom human reviewers and automated checkers. Experimental results demonstrate aremarkable repair rate of 72.97% with the best prompt, highlighting asubstantial improvement in the effectiveness and practicality of automaticrepair techniques.

The Right Prompts for the Job: Repair Code-Review Defects with Large Language Model