  Large Language Models (LLMs) are rapidly becoming ubiquitous both as
stand-alone tools and as components of current and future software systems. To
enable usage of LLMs in the high-stake or safety-critical systems of 2030, they
need to undergo rigorous testing. Software Engineering (SE) research on testing
Machine Learning (ML) components and ML-based systems has systematically
explored many topics such as test input generation and robustness. We believe
knowledge about tools, benchmarks, research and practitioner views related to
LLM testing needs to be similarly organized. To this end, we present a taxonomy
of LLM testing topics and conduct preliminary studies of state of the art and
practice approaches to research, open-source tools and benchmarks for LLM
testing, mapping results onto this taxonomy. Our goal is to identify gaps
requiring more research and engineering effort and inspire a clearer
communication between LLM practitioners and the SE research community.
