  Test smells are coding issues that typically arise from inadequate practices,
a lack of knowledge about effective testing, or deadline pressures to complete
projects. The presence of test smells can negatively impact the maintainability
and reliability of software. While there are tools that use advanced static
analysis or machine learning techniques to detect test smells, these tools
often require effort to be used. This study aims to evaluate the capability of
Large Language Models (LLMs) in automatically detecting test smells. We
evaluated ChatGPT-4, Mistral Large, and Gemini Advanced using 30 types of test
smells across codebases in seven different programming languages collected from
the literature. ChatGPT-4 identified 21 types of test smells. Gemini Advanced
identified 17 types, while Mistral Large detected 15 types of test smells.
Conclusion: The LLMs demonstrated potential as a valuable tool in identifying
test smells.
