  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.
