example.json

{
    "Large Language Models Based Fuzzing Techniques: A Survey": {
        "author": "Misu, Md Rakib Hossain and Lopes, Cristina V. and Ma, Iris and Noble, James",
        "title": "Large Language Models Based Fuzzing Techniques: A Survey",
        "url": "https://arxiv.org/pdf/2402.00350",
        "abstract": "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 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, including 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.",
        "labels": [
            "program testing",
            "fuzzing",
            "survey"
        ],
        "venue": "arXiv2024"
    }
}