用大模型自动检测修复代码问题，降低人工成本。
WALL: A Web Application for Automated Quality Assurance using Large Language Models
- 融合SonarQube与GPT系列模型，构建代码质量自动化流水线
- 在7599个问题上实现高精度修复，显著减少人力投入
- 适合希望提升代码审查效率的开发团队和运维人员
随着软件项目日益复杂，代码文件中的问题数量和类型急剧增加。本文提出WALL，一个集成SonarQube与大语言模型（如GPT-3.5 Turbo和GPT-4o）的网页应用，用于自动化检测、修复与评估代码问题。WALL包含三个模块：问题提取工具、代码修正工具和代码对比工具，形成完整的修复流程。在563个文件、超过7,599个问题上的实验表明，该系统能有效降低人工工作量，同时保持高质量的代码修订。结果表明，结合低成本与高性能的LLM可显著降低成本并提升修复率。未来工作将引入开源LLM并实现完全自动化，推动代码质量管理的全面智能化。
原文摘要 · Abstract (English)
As software projects become increasingly complex, the volume and variety of issues in code files have grown substantially. Addressing this challenge requires efficient issue detection, resolution, and evaluation tools. This paper presents WALL, a web application that integrates SonarQube and large language models (LLMs) such as GPT-3.5 Turbo and GPT-4o to automate these tasks. WALL comprises three modules: an issue extraction tool, code issues reviser, and code comparison tool. Together, they enable a seamless pipeline for detecting software issues, generating automated code revisions, and evaluating the accuracy of revisions. Our experiments, conducted on 563 files with over 7,599 issues, demonstrate WALL's effectiveness in reducing human effort while maintaining high-quality revisions. Results show that employing a hybrid approach of cost-effective and advanced LLMs can significantly lower costs and improve revision rates. Future work aims to enhance WALL's capabilities by integrating open-source LLMs and eliminating human intervention, paving the way for fully automated code quality management.
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