Enhancing Code Quality Through Automated Refactoring Using Transformer-Based Language Models

Maintaining high-quality source code is crucial for software reliability, scalability, and maintainability. Traditional refactoring methods, which involve manual code improvement or rule-based automation, often fall short due to their inability to understand the contextual semantics of code. These approaches are rigid, language-specific, and prone to inconsistencies, especially in large and complex codebases. As a result, developers spend significant time and effort identifying code smells, restructuring poorly written segments, and ensuring behavior preservation shows an accuracy of 97%. To address these limitations, this study proposes an automated code refactoring framework powered by Transformer-based language models. Leveraging models such as CodeT5, which are pre-trained on massive code corpora, this approach captures both syntax and semantic patterns to suggest intelligent, context-aware code transformations. The model is fine-tuned using a curated dataset of original and refactored code pairs to learn efficient refactoring strategies. The methodology involves preprocessing raw source code, tokenizing it for model input, and generating improved versions of the code using the trained Transformer model. Output suggestions are validated using Abstract Syntax Tree (AST) analysis and unit testing to ensure behavioral equivalence. Code quality improvements are quantified using metrics like maintainability index, cyclomatic complexity, and duplication rate. Experimental results demonstrate that the proposed method significantly enhances code readability and maintainability while reducing developer effort, outperforming traditional rule-based refactoring tools.