TestART: Improving LLM-based Unit Test via Co-evolution of Automated Generation and Repair Iteration
<|reference_start|>TestART: Improving LLM-based Unit Test via Co-evolution of Automated Generation and Repair Iteration: Unit test is crucial for detecting bugs in individual program units but consumes time and effort. The existing automated unit test generation methods are mainly based on search-based software testing (SBST) and language models to liberate developers. Recently, large language models (LLMs) have demonstrated remarkable reasoning and generation capabilities. However, several problems limit their ability to generate high-quality test cases: (1) LLMs may generate invalid test cases under insufficient context, resulting in compilation errors; (2) Lack of test and coverage feedback information may cause runtime errors and low coverage rates. (3) The repetitive suppression problem causes LLMs to get stuck into the repetition loop of self-repair or re-generation attempts. In this paper, we propose TestART, a novel unit test generation method that leverages the strengths of LLMs while overcoming the limitations mentioned. TestART improves LLM-based unit test via co-evolution of automated generation and repair iteration. TestART leverages the template-based repair technique to fix bugs in LLM-generated test cases, using prompt injection to guide the next-step automated generation and avoid repetition suppression. Furthermore, TestART extracts coverage information from the passed test cases and utilizes it as testing feedback to enhance the sufficiency of the final test case. This synergy between generation and repair elevates the quality, effectiveness, and readability of the produced test cases significantly beyond previous methods. In comparative experiments, the pass rate of TestART-generated test cases is 78.55%, which is approximately 18% higher than both the ChatGPT-4.0 model and the same ChatGPT-3.5-based method ChatUniTest. It also achieves an impressive line coverage rate of 90.96% on the focal methods that passed the test, exceeding EvoSuite by 3.4%.<|reference_end|>
@article{gu2024improving,
    title={Improving LLM-based Unit test generation via Template-based Repair},
    author={Siqi Gu, Chunrong Fang, Quanjun Zhang, Fangyuan Tian, Jianyi Zhou and
  Zhenyu Chen},
    journal={arXiv preprint arXiv:2408.03095},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03095},
    primaryClass={cs.SE}
}