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Recent advancements in large language models (LLMs) have demonstratedexceptional success in a wide range of general domain tasks, such as questionanswering and following instructions. Moreover, LLMs have shown potential invarious software engineering applications. In this study, we present asystematic comparison of test suites generated by the ChatGPT LLM and thestate-of-the-art SBST tool EvoSuite. Our comparison is based on severalcritical factors, including correctness, readability, code coverage, and bugdetection capability. By highlighting the strengths and weaknesses of LLMs(specifically ChatGPT) in generating unit test cases compared to EvoSuite, thiswork provides valuable insights into the performance of LLMs in solvingsoftware engineering problems. Overall, our findings underscore the potentialof LLMs in software engineering and pave the way for further research in thisarea.

ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation