Software testing is an important part of the development cycle, yet it requires specialized expertise and substantial developer effort to adequately test software. 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 for testing. We identify other tangible benefits that LLM-driven testing agents can bestow, and also discuss 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开发人员投入。近期对大语言模型（LLM）能力的探索表明，它们可作为自动化测试助手，提供有价值的信息甚至推动测试流程。为凸显该技术的潜力，我们基于自主化程度构建了LLM测试代理分类体系，并阐释更高自主性如何为开发者带来实际效益。通过一个将LLM用作测试助手的实例，展示了对话式测试框架如何辅助开发人员，同时揭示了常遭批评的LLM幻觉现象在测试中的潜在优势。本文还总结了LLM驱动的测试代理所能提供的其他显著优势，并探讨了其可能存在的局限性。