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
Parents
64
Skyfire: Data-Driven Seed Generation for Fuzzing
Junjie Wang, Bihuan Chen, Lei Wei et al. · 2017 · 307 citations
Christopher Lidbury, Andrei Lascu, Nathan Chong et al. · 2015 · 146 citations
2019 · 125 citations · Full text
1–5 of 64
234
7
h-index
4
h-index
4
h-index
3
h-index
3
h-index
1–5 of 234
81
Tsinghua University
Beijing, China
6 h-index
20 publications
1K citations
University of California, Berkeley
Berkeley, United States
5 h-index
11 publications
607 citations

National University of Defense Technology
Changsha, China
4 h-index
11 publications
295 citations
University of Illinois Urbana-Champaign
Urbana, United States
4 h-index
9 publications
1.4K citations
Nanyang Technological University
Singapore, Singapore
4 h-index
12 publications
1.4K citations
Nanjing University
Nanjing, China
3 h-index
10 publications
285 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
4 publications
181 citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
3 h-index
8 publications
369 citations
1–8 of 81
Repository · 6 publications · 150 citations
6
h-index
Conference · 4 publications · 178 citations
4
h-index
PACMPL · 3 publications · 188 citations
4
h-index
2 publications · 37 citations
3
h-index
2 publications · 49 citations
2
h-index
1–5