Enhancing Software Maintainability Through LLM-Assisted Code Refactoring

doi:10.1007/978-3-032-12089-2_10

Enhancing Software Maintainability Through LLM-Assisted Code Refactoring

2026-01-01

0

PRE

AI

DOI：10.1007/978-3-032-12089-2_10

Original

Original request for help

Save

Abstract

En 中文

High code quality, particularly in terms of maintainability, is crucial for ensuring that software remains efficient and adaptable over time, while minimizing long-term maintenance costs. As artificial intelligence continues to evolve, its application in software development offers new opportunities to improve code quality. This study investigates the use of Large Language Models (LLMs) to enhance software maintainability through code refactoring. The results indicate that LLMs can be effectively utilized for this purpose, with effectiveness varying depending on the model and the evaluation metric used. Although the study is based on a limited set of Python projects and specific prompting strategies, it provides a meaningful step toward understanding the broader applicability of LLMs in this context.

Keywords:

LLMs

Code Maintainability

Code Quality

Technical Debt

Enhancing Software Maintainability Through LLM-Assisted Code Refactoring

Abstract

Journal

Organization