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Unit testing is essential in detecting bugs in functionally-discrete programunits. Manually writing high-quality unit tests is time-consuming andlaborious. Although traditional techniques can generate tests with reasonablecoverage, they exhibit low readability and cannot be directly adopted bydevelopers. Recent work has shown the large potential of large language models(LLMs) in unit test generation, which can generate more human-like andmeaningful test code. ChatGPT, the latest LLM incorporating instruction tuningand reinforcement learning, has performed well in various domains. However, Itremains unclear how effective ChatGPT is in unit test generation.  In this work, we perform the first empirical study to evaluate ChatGPT'scapability of unit test generation. Specifically, we conduct a quantitativeanalysis and a user study to systematically investigate the quality of itsgenerated tests regarding the correctness, sufficiency, readability, andusability. The tests generated by ChatGPT still suffer from correctness issues,including diverse compilation errors and execution failures. Still, the passingtests generated by ChatGPT resemble manually-written tests by achievingcomparable coverage, readability, and even sometimes developers' preference.Our findings indicate that generating unit tests with ChatGPT could be verypromising if the correctness of its generated tests could be further improved.  Inspired by our findings above, we propose ChatTESTER, a novel ChatGPT-basedunit test generation approach, which leverages ChatGPT itself to improve thequality of its generated tests. ChatTESTER incorporates an initial testgenerator and an iterative test refiner. Our evaluation demonstrates theeffectiveness of ChatTESTER by generating 34.3% more compilable tests and 18.7%more tests with correct assertions than the default ChatGPT.

No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation