LLM Code Smells: A Taxonomy and Detection Approach
The paper titled 'LLM Code Smells: A Taxonomy and Detection Approach' discusses the integration of Large Language Models (LLMs) in software systems. It presents a taxonomy of nine LLM code smells and introduces a tool called SpecDetect4LLM for their detection. The study found that 73.5% of analyzed systems exhibited LLM code smells, with high detection precision and recall rates.
- ▪The paper consolidates and refines the concept of LLM code smells.
- ▪A static source code analysis tool named SpecDetect4LLM was created for detection.
- ▪The study analyzed 692 open-source software projects, revealing a prevalence of 73.5% for LLM code smells.
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Computer Science > Software Engineering arXiv:2605.22976 (cs) [Submitted on 21 May 2026] Title:LLM Code Smells: A Taxonomy and Detection Approach Authors:Zacharie Chenail-Larcher, Brahim Mahmoudi, Naouel Moha, Quentin Stiévenart, Florent Avellaneda View a PDF of the paper titled LLM Code Smells: A Taxonomy and Detection Approach, by Zacharie Chenail-Larcher and 4 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) are increasingly integrated into software systems for diverse purposes, due to their versatility, flexibility, and ability to simulate human reasoning to some extent. However, poor integration of LLM inference in source code can undermine software system quality.
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