Random Fuzzing

Random Fuzzing is a fuzz testing method that generates test inputs by applying purely random modifications to input data, without leveraging program-specific knowledge or feedback to guide the generation process.

36

Publications

3.5K

Citations

130

Authors

45

Institutions

Publications per year

2017–2026

20

Variants

Random Fuzz Testing

Authors

130

  1. GG
    Gustavo Grieco

    Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina

    3 publications · 197 citations

    3

    h-index

  2. JR
    John Regehr

    University of Utah, United States

    3 publications · 297 citations

    3

    h-index

  3. AR
    Abhik Roychoudhury

    National University of Singapore, Singapore

    3 publications · 982 citations

    3

    h-index

  4. NC
    Nathan Chong

    Imperial College London, United Kingdom

    2 publications · 211 citations

    2

    h-index

  5. SE
    Shawn Embleton

    University of Central Florida, United States

    2 publications · 98 citations

    2

    h-index

1–5 of 130

Venues

  1. ACM SIGPLAN Notices

    3 publications · 102 citations

    3

    h-index

  2. IEEE Transactions on Software Engineering

    TSE · 2 publications · 352 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 32 citations

    2

    h-index

  4. IEEE Access

    2 publications · 67 citations

    2

    h-index

  5. Proceedings of the ACM on Programming Languages

    PACMPL · 1 publications · 125 citations

    1

    h-index

1–5