Mutation-based Fuzzing
Mutation-Based Fuzzing is a fuzz testing technique that generates test inputs by applying incremental, random modifications to existing valid inputs, thereby exploring new program behaviors while preserving syntactic structure.
115
Publications
6.8K
Citations
394
Authors
149
Institutions
Publications per year
2017–2026
95
Variants
Mutation-based Fuzz Testing
Parents
115
Angora: Efficient Fuzzing by Principled Search
Skyfire: Data-Driven Seed Generation for Fuzzing
Junjie Wang, Bihuan Chen, Lei Wei et al. · 2017 · 307 citations
DLFuzz: differential fuzzing testing of deep learning systems
Jianmin Guo, Yu Jiang, Yue Zhao et al. · 2018 · 291 citations · Full text
Steelix: program-state based binary fuzzing
Yuekang Li, Bihuan Chen, Mahinthan Chandramohan et al. · 2017 · 277 citations
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149
Tsinghua University
Beijing, China
7 h-index
27 publications
2.4K citations
Nanyang Technological University
Singapore, Singapore
6 h-index
21 publications
3.8K citations

Beijing University of Posts and Telecommunications
Beijing, China
6 h-index
14 publications
237 citations
Georgia Institute of Technology
Atlanta, United States
6 h-index
20 publications
1.3K citations

National University of Defense Technology
Changsha, China
5 h-index
22 publications
487 citations
University of California, Davis
Davis, United States
5 h-index
7 publications
513 citations
University of California, Berkeley
Berkeley, United States
4 h-index
11 publications
1.4K citations

Shanghai Jiao Tong University
Shanghai, China
4 h-index
6 publications
727 citations
1–8 of 149
Repository · 10 publications · 257 citations
9
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Conference · 5 publications · 171 citations
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PACMPL · 3 publications · 93 citations
3
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3 publications · 100 citations
3
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2 publications · 59 citations
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