Improving structural code quality of java source codes through automated detection and refactoring of code smells leveraging large language models (LLMs)

Abstract

Structural and design issues in source code, commonly referred to as code smells, can reduce software maintainability, readability, and overall quality. Traditional code analysis tools rely primarily on predefined rules and heuristics for smell detection, which often limits their ability to understand broader code context and provide meaningful refactoring support. Recent advances in Large Language Models (LLMs) have created new opportunities for intelligent code analysis and automated software maintenance. This thesis presents an LLM-based approach for the detection and refactoring of code smells in source code. The proposed framework employs two specialized models: a code smell detection model trained to identify common structural and design issues, and a refactoring model trained on paired before-and-after code examples to generate refactored code and identify the refactoring technique applied. The detection model recognizes several categories of code smells, while the refactoring model generates improved code by applying appropriate refactoring transformations. We are working to propose an automated engine that uses LLM to detect and refactor key code smells in deployed Java projects. Our proposed framework combines static code heuristics with parameter-efficient fine-tuning using for code smell detection. Additionally, a separate LoRA fine-tuned model is trained on before-and-after code pairs to perform refactoring , generating refactored code along with the corresponding refactoring type. We aim to find if the semantic understanding of LLMs are capable enough to find the common smells in deployed Java source codes and refactor to improve the quality of the codes. We will construct a dataset for internal testing and benchmarking. This work illustrates how LLMs can serve as intelligent assistants in software maintenance, enabling automated detection and context-aware refactoring.