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Link to original content: https://doi.org/10.48550/arXiv.2312.17485
[2312.17485] The Right Prompts for the Job: Repair Code-Review Defects with Large Language Model
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Computer Science > Software Engineering

arXiv:2312.17485 (cs)
[Submitted on 29 Dec 2023]

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

Authors:Zelin Zhao, Zhaogui Xu, Jialong Zhu, Peng Di, Yuan Yao, Xiaoxing Ma
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Abstract:Automatic program repair (APR) techniques have the potential to reduce manual efforts in uncovering and repairing program defects during the code review (CR) process. However, the limited accuracy and considerable time costs associated with existing APR approaches hinder their adoption in industrial practice. One key factor is the under-utilization of review comments, which provide valuable insights into defects and potential fixes. Recent advancements in Large Language Models (LLMs) have enhanced their ability to comprehend natural and programming languages, enabling them to generate patches based on review comments. This paper conducts a comprehensive investigation into the effective utilization of LLMs for repairing CR defects. In this study, various prompts are designed and compared across mainstream LLMs using two distinct datasets from human reviewers and automated checkers. Experimental results demonstrate a remarkable repair rate of 72.97% with the best prompt, highlighting a substantial improvement in the effectiveness and practicality of automatic repair techniques.
Comments: 10 pages with 1 page for references
Subjects: Software Engineering (cs.SE)
MSC classes: 68T01
ACM classes: I.2.5; D.2.0
Cite as: arXiv:2312.17485 [cs.SE]
  (or arXiv:2312.17485v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2312.17485

Submission history

From: Zelin Zhao [view email]
[v1] Fri, 29 Dec 2023 06:12:15 UTC (138 KB)
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