SmellDetector: Multi-Label Code Smell Detection and Refactoring with Large Language Models
Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in many tasks such as code generation and automated program repair. However, code LLMs have ignored another important task in programmers' daily development work, which is to improve the maintainability, readability, and scalability of the program. All of these characteristics are related to code smells and we study how to improve them by detecting and removing code smells. Most works on code smells still rely on using measures formulated by experts as features, but lack of use of the rich prior knowledge contained in code LLMs. In this paper, we propose SmellDetector, a comprehensive model for both code smell detection and refactoring opportunities detection in Java. We train the model with the designed prompt which contains both code smells of class-level and method-level in the same code snippet, including more than 20 types. We achieve state-of-the-art performance on the code smell detection task and change the basic paradigm of code smell detection from binary classification problem to multi-label classification. Finally, it has been verified through experiments that good code smell detection helps to detect refactoring opportunities.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: code generation and understanding
Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models
Languages Studied: English
Submission Number: 4177
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