Feedback-driven Fuzzing
Feedback-Driven Fuzzing is a fuzz testing method that iteratively generates test inputs by leveraging runtime feedback from prior executions to guide the exploration of program paths, thereby distinguishing itself from non-guided fuzzing techniques through its adaptive, feedback-informed input mutation strategy.
28
Publications
2.5K
Citations
120
Authors
45
Institutions
Publications per year
2017–2026
27
Parents
28
DeepHunter: a coverage-guided fuzz testing framework for deep neural networks
Xiaofei Xie, Lei Ma, Felix Juefei-Xu et al. · 2019 · 443 citations · Full text
REDQUEEN: Fuzzing with Input-to-State Correspondence
Cornelius Aschermann, Sergej Schumilo, Tim Blazytko et al. · 2019 · 314 citations · Full text
RESTler: Stateful REST API Fuzzing
Vaggelis Atlidakis, Patrice Godefroid, Marina Polishchuk · 2019 · 212 citations
Echidna: effective, usable, and fast fuzzing for smart contracts
Gustavo Grieco, Will Song, Artur Cygan et al. · 2020 · 197 citations
NAUTILUS: Fishing for Deep Bugs with Grammars
Cornelius Aschermann, Tommaso Frassetto, Thorsten Holz et al. · 2019 · 183 citations · Full text
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45
Tsinghua University
Beijing, China
4 h-index
18 publications
429 citations
University of California, Berkeley
Berkeley, United States
3 h-index
10 publications
1.2K citations
Ruhr University Bochum
Bochum, Germany
3 h-index
12 publications
2.8K citations
EURECOM
Valbonne, France
3 h-index
8 publications
212 citations
Nanyang Technological University
Singapore, Singapore
2 h-index
6 publications
1.4K citations

Zhejiang University
Hangzhou, China
2 h-index
2 publications
69 citations
Technische Universität Berlin
Berlin, Germany
2 h-index
5 publications
88 citations

University of Waterloo
Waterloo, Canada
2 h-index
2 publications
42 citations
1–8 of 45
Repository · 4 publications · 86 citations
4
h-index
Conference · 2 publications · 191 citations
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PACMPL · 1 publications · 67 citations
1
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TOSEM · 1 publications · 40 citations
1
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Conference · 1 publications · 15 citations
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