AI-Driven Code Refactoring: Using Graph Neural Networks to Enhance Software Maintainability

2025-04-14

AI Summary

This work addresses the declining maintainability of software caused by high cyclomatic complexity and coupling in code refactoring. We propose the first end-to-end Graph Neural Network (GNN)-driven, semantics-aware refactoring method. By modeling Abstract Syntax Trees (ASTs) as graphs and integrating AST embeddings with static analysis, our approach automatically identifies and optimizes high-complexity, high-coupling code fragments. Unlike conventional rule-based or shallow-model approaches, ours is the first to systematically apply GNNs across the entire refactoring decision pipeline. Evaluated on 2 million Python code snippets, our method achieves 92% refactoring accuracy, reduces average cyclomatic complexity by 35%, and decreases coupling by 33%. These improvements significantly outperform established baselines—including SonarQube and decision tree–based methods—demonstrating both technical novelty and practical efficacy in automated, semantics-guided code refactoring.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Using GNNs to improve software maintainability via code refactoring
• Comparing GNN performance against rule-based and decision tree methods
• Reducing code complexity and coupling with AI-driven refactoring

Innovation

• AI-driven code refactoring with Graph Neural Networks
• Uses abstract syntax trees for maintainability enhancement
• Achieves 92% accuracy in complexity reduction