WALL: A Web Application for Automated Quality Assurance using Large Language Models
This addresses the challenge of efficient code quality assurance for software developers, though it is incremental as it builds on existing tools like SonarQube and LLMs.
The paper tackles the problem of managing code quality in complex software projects by introducing WALL, a web application that integrates SonarQube and large language models to automate issue detection, revision, and evaluation, achieving high-quality revisions on 563 files with over 7,599 issues while reducing human effort and costs.
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.