Generation-based Fuzzing

Generation-Based Fuzzing is a fuzz testing method that systematically generates test inputs by constructing valid syntactic and semantic instances of a target format or protocol from a predefined model, distinguishing it from mutation-based approaches by emphasizing structure-aware input creation over random alteration of existing samples.

64

Publications

2.8K

Citations

234

Authors

81

Institutions

Publications per year

2017–2026

57

Variants

Generation-based Fuzz Testing

Authors

234

  1. YJ
    Yu Jiang

    Tsinghua University, China

    5 publications · 201 citations

    7

    h-index

  2. LZ
    Lingming Zhang

    University of Illinois Urbana-Champaign, United States

    4 publications · 208 citations

    4

    h-index

  3. YD
    Yinlin Deng

    University of Illinois Urbana-Champaign, United States

    4 publications · 392 citations

    4

    h-index

  4. KS
    Koushik Sen

    University of California, Berkeley, United States

    3 publications · 53 citations

    3

    h-index

  5. RP
    Rohan Padhye

    University of California, Berkeley, United States

    3 publications · 53 citations

    3

    h-index

1–5 of 234

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 150 citations

    6

    h-index

  2. 4

    h-index

  3. Proceedings of the ACM on Programming Languages

    PACMPL · 3 publications · 188 citations

    4

    h-index

  4. Computers & Security

    2 publications · 37 citations

    3

    h-index

  5. Neurocomputing

    2 publications · 49 citations

    2

    h-index

1–5