Software testing is an important part of the development cycle, yet it requires specialized expertise and substantial developer effort to adequately test software. The recent discoveries of the capabilities of large language models (LLMs) suggest that they can be used as automated testing assistants, and thus provide helpful information and even drive the testing process. To highlight the potential of this technology, we present a taxonomy of LLM-based testing agents based on their level of autonomy, and describe how a greater level of autonomy can benefit developers in practice. An example use of LLMs as a testing assistant is provided to demonstrate how a conversational framework for testing can help developers. This also highlights how the often criticized hallucination of LLMs can be beneficial while testing. We identify other tangible benefits that LLM-driven testing agents can bestow, and also discuss some potential limitations.

翻译：软件测试是开发周期中的重要环节，但充分测试软件需要专业知识和大量的开发人员投入。大语言模型（LLM）的最新能力表明，它们可作为自动化测试助手提供有用信息，甚至驱动测试流程。为凸显该技术的潜力，本文提出基于LLM的测试智能体分类体系（按自主程度划分），并阐述更高自主性如何在实际中为开发者带来益处。通过将LLM作为测试助手的示例应用，展示了对话式测试框架如何辅助开发者，同时揭示LLM常被诟病的"幻觉"现象在测试中的有益之处。我们进一步归纳了LLM驱动测试智能体的其他实际优势，并讨论了其潜在局限性。