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.

翻译：在现代软件扮演关键角色的时代，软件安全与漏洞分析已成为软件开发的必要环节。模糊测试作为一种高效的软件测试方法，被广泛应用于各个领域。同时，大语言模型的快速发展促进了其在软件测试领域的应用，展现出卓越性能。考虑到现有模糊测试技术尚未完全实现自动化，且软件漏洞持续演化，基于大语言模型生成的模糊测试技术应用趋势日益显著。本综述系统梳理了融合大语言模型与模糊测试的软件测试方法。通过对截至2024年的最新研究方法进行归纳，本文从大语言模型、模糊测试以及基于大语言模型生成的模糊测试三个领域展开文献统计分析与讨论。本综述还探讨了未来大语言模型生成的模糊测试技术大规模部署与应用的潜在可能性。