Seed Corpus Generation
Seed Corpus Generation is the systematic process of creating an initial collection of input data specifically designed to serve as a foundational basis for fuzz testing, distinguished by its role in enhancing the effectiveness and efficiency of test case exploration through the provision of semantically and syntactically relevant starting inputs.
16
Publications
834
Citations
60
Authors
22
Institutions
Publications per year
2017–2026
10
Parents
16
Skyfire: Data-Driven Seed Generation for Fuzzing
Junjie Wang, Bihuan Chen, Lei Wei et al. · 2017 · 307 citations
Seed selection for successful fuzzing
Adrian Herrera, Hendra Gunadi, Shane Magrath et al. · 2021 · 95 citations · Full text
Semi-automated named entity annotation
Kuzman Ganchev, Fernando Pereira, Mark A. Mandel et al. · 2007 · 32 citations · Full text
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Nanyang Technological University
Singapore, Singapore
3 h-index
6 publications
1.3K citations

Chinese Academy of Sciences
Beijing, China
2 h-index
7 publications
130 citations
Defence Science and Technology Group
Canberra, Australia
1 h-index
1 publications
95 citations

Beijing Technology and Business University
Beijing, China
1 h-index
1 publications
25 citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
1 h-index
1 publications
95 citations

Data61
Sydney, Australia
1 h-index
2 publications
190 citations
University of Pennsylvania
Philadelphia, United States
1 h-index
3 publications
96 citations

Saarland University
Saarbrücken, Germany
1 h-index
1 publications
14 citations
1–8 of 22
1 publications · 18 citations
2
h-index
1 publications · 16 citations
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Conference · 1 publications · 165 citations
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Repository · 1 publications · 10 citations
1
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Repository · 1 publications · 28 citations
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