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Teaching Code Refactoring Using LLMs

Name: Teaching Code Refactoring Using LLMs
Keywords: Machine Learning, Software Engineering (cs.SE), FOS: Computer and information sciences, Software Engineering, Machine Learning (cs.LG)

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2025Embargo end date: 01 Jan 2025Publisher:arXiv

Authors: Khairnar, Anshul; Rajoju, Aarya; Gehringer, Edward F.;

doi: 10.48550/arxiv.2508.09332

arXiv: http://arxiv.org/abs/2508.09332

Teaching Code Refactoring Using LLMs

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Abstract

This Innovative Practice full paper explores how Large Language Models (LLMs) can enhance the teaching of code refactoring in software engineering courses through real-time, context-aware feedback. Refactoring improves code quality but is difficult to teach, especially with complex, real-world codebases. Traditional methods like code reviews and static analysis tools offer limited, inconsistent feedback. Our approach integrates LLM-assisted refactoring into a course project using structured prompts to help students identify and address code smells such as long methods and low cohesion. Implemented in Spring 2025 in a long-lived OSS project, the intervention is evaluated through student feedback and planned analysis of code quality improvements. Findings suggest that LLMs can bridge theoretical and practical learning, supporting a deeper understanding of maintainability and refactoring principles.

Accepted for presentation at the Frontiers in Education Conference, Nashville, Tennessee, USA, 2-5 November 2025

Keywords

Machine Learning, Software Engineering (cs.SE), FOS: Computer and information sciences, Software Engineering, Machine Learning (cs.LG)

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