Input Seed Selection

Input Seed Selection is a foundational method in fuzz testing that identifies and prioritizes initial input instances to guide the generation of new test cases through systematic mutation and exploration of program behavior.

37

Publications

2.5K

Citations

140

Authors

52

Institutions

Publications per year

2017–2026

21

Authors

140

  1. SS
    Sherri Sparks

    University of Central Florida, United States

    2 publications · 98 citations

    2

    h-index

  2. DB
    David Brumley

    Carnegie Mellon University, United States

    2 publications · 392 citations

    2

    h-index

  3. JM
    Jonathan M. Foote

    Carnegie Mellon University, United States

    2 publications · 179 citations

    2

    h-index

  4. RC
    Ryan Cunningham

    Manchester Metropolitan University, United Kingdom

    2 publications · 98 citations

    2

    h-index

  5. CC
    Cliff C. Zou

    University of Central Florida, United States

    2 publications · 98 citations

    2

    h-index

1–5 of 140

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 73 citations

    3

    h-index

  2. Information and Software Technology

    2 publications · 34 citations

    2

    h-index

  3. 2

    h-index

  4. Computers & Security

    1 publications · 16 citations

    1

    h-index

  5. USENIX Security Symposium

    Conference · 1 publications · 79 citations

    1

    h-index

1–5