用图神经网络分析代码结构，自动优化可维护性。
AI-Driven Code Refactoring: Using Graph Neural Networks to Enhance Software Maintainability
- 将代码转为抽象语法树，用图神经网络识别重构点
- 降低35%复杂度、33%耦合度，准确率达92%
- 适合希望自动化提升代码质量的开发团队
本研究探索图神经网络（GNNs）作为代码重构的变革性工具，利用抽象语法树（ASTs）提升软件可维护性。基于来自CodeSearchNet的200万代码片段及自建的7.5万文件GitHub Python语料库，对比GNN与基于规则的SonarQube和决策树方法。评估指标包括环路复杂度（目标低于10）、耦合度（目标低于5）和重构精度。GNN达到92%准确率，使复杂度降低35%，耦合度下降33%，优于SonarQube（78%，16%）和决策树（85%，25%）。预处理修复了60%的语法错误。柱状图、表格和AST可视化清晰呈现结果。该方法为构建更清洁代码库提供了可扩展的AI路径，对软件工程至关重要。
原文摘要 · Abstract (English)
This study explores Graph Neural Networks (GNNs) as a transformative tool for code refactoring, using abstract syntax trees (ASTs) to boost software maintainability. It analyzes a dataset of 2 million snippets from CodeSearchNet and a custom 75000-file GitHub Python corpus, comparing GNNs against rule-based SonarQube and decision trees. Metrics include cyclomatic complexity (target below 10), coupling (target below 5), and refactoring precision. GNNs achieve 92% accuracy, reducing complexity by 35% and coupling by 33%, outperforming SonarQube (78%, 16%) and decision trees (85%, 25%). Preprocessing fixed 60% of syntax errors. Bar graphs, tables, and AST visuals clarify results. This offers a scalable AI-driven path to cleaner codebases, which is crucial for software engineering.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。