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
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2.5K
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140
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52
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2017–2026
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Marcel Böhme, Van-Thuan Pham, Manh-Dung Nguyen et al. · 2017 · 717 citations
Skyfire: Data-Driven Seed Generation for Fuzzing
Junjie Wang, Bihuan Chen, Lei Wei et al. · 2017 · 307 citations
Program-Adaptive Mutational Fuzzing
Sang Kil, Maverick Woo, David Brumley · 2015 · 239 citations · Full text
Ijon: Exploring Deep State Spaces via Fuzzing
Cornelius Aschermann, Sergej Schumilo, Ali Abbasi et al. · 2020 · 119 citations · Full text
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Carnegie Mellon University
Pittsburgh, United States
3 h-index
8 publications
1.4K citations

Chinese Academy of Sciences
Beijing, China
3 h-index
8 publications
141 citations
Institute of Information Engineering
Beijing, China
2 h-index
6 publications
91 citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
2 h-index
2 publications
119 citations
Tianjin University
Tianjin, China
2 h-index
6 publications
102 citations
Nanyang Technological University
Singapore, Singapore
2 h-index
5 publications
1.2K citations

University of Central Florida
Orlando, United States
2 h-index
8 publications
392 citations

Data61
Sydney, Australia
1 h-index
2 publications
190 citations
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Repository · 3 publications · 73 citations
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2 publications · 34 citations
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Repository · 2 publications · 179 citations
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1 publications · 16 citations
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Conference · 1 publications · 79 citations
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