Constraint-guided Fuzzing
Constraint-Guided Fuzzing is a fuzz testing method that systematically generates inputs by leveraging symbolic constraints derived from program semantics to direct the exploration of execution paths, thereby distinguishing itself from other fuzzing techniques through its reliance on constraint solving to enhance test coverage and bug detection.
13
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587
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53
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20
Institutions
Publications per year
2017–2026
9
Parents
13
DocTer: documentation-guided fuzzing for testing deep learning API functions
Danning Xie, Yitong Li, Mijung Kim et al. · 2022 · 73 citations · Full text
GRIMOIRE: Synthesizing Structure while Fuzzing.
Tim Blazytko · Open MIND · 2019 · 54 citations · Full text
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Microsoft (United States)
Redmond, United States
2 h-index
6 publications
618 citations
Purdue University West Lafayette
West Lafayette, United States
2 h-index
5 publications
347 citations

University of Waterloo
Waterloo, Canada
1 h-index
3 publications
219 citations
Ruhr University Bochum
Bochum, Germany
1 h-index
1 publications
54 citations
The University of Texas at Dallas
Richardson, United States
1 h-index
2 publications
20 citations
Samsung (United States)
Ridgefield Park, United States
1 h-index
1 publications
17 citations
Georgia Institute of Technology
Atlanta, United States
1 h-index
6 publications
258 citations

Pennsylvania State University
State College, United States
1 h-index
6 publications
258 citations
1–8 of 20
1 publications · 19 citations
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Repository · 1 publications · 54 citations
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Conference · 1 publications · 89 citations
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1 publications · 10 citations
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1 publications · 11 citations
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