  In the modern era where software plays a pivotal role, software security and
vulnerability analysis have become essential for software development. Fuzzing
test, as an efficient software testing method, are widely used in various
domains. Moreover, the rapid development of Large Language Models (LLMs) has
facilitated their application in the field of software testing, demonstrating
remarkable performance. Considering that existing fuzzing test techniques are
not entirely automated and software vulnerabilities continue to evolve, there
is a growing trend towards employing fuzzing test generated based on large
language models. This survey provides a systematic overview of the approaches
that fuse LLMs and fuzzing tests for software testing. In this paper, a
statistical analysis and discussion of the literature in three areas, namely
LLMs, fuzzing test, and fuzzing test generated based on LLMs, are conducted by
summarising the state-of-the-art methods up until 2024. Our survey also
investigates the potential for widespread deployment and application of fuzzing
test techniques generated by LLMs in the future.
