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On the reliability of coverage-based fuzzer benchmarking
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Given a program where none of our fuzzers finds any bugs, how do we know which fuzzer is better? In practice, we often look to code coverage as a proxy measure of fuzzer effectiveness and consider the fuzzer which achieves more coverage as the better one.
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“Complete line coverage alone does not guarantee effective testing [4].”
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Ding, L., Yang, W., & Xue, Y. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00145
Towards Path-Aware Coverage-Guided Fuzzing
Priamo, G., D’Elia, D. C., Payer, M., & Querzoni, L. (2026) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1109/cgo68049.2026.11395191
Sagitta: Facilitating Post-Fuzzing Root Cause Analysis via Data Flow Differencing
Aoki, K., & Shinagawa, T. (2025) · Annual Computer Security Applications Conference · doi:10.1109/acsac67867.2025.00017
Optimized mutation scheduling for fuzzing based on estimation of distribution algorithm
Lv, G., Chen, J., Chen, H., Cai, S., & Zhang, W. (2026) · Cybersecurity · doi:10.1186/s42400-025-00461-7
SeedAIchemy: LLM-Driven Seed Corpus Generation for Fuzzing
Wen, A., Alzahrani, N. A., Jiang, J., Joe, A., Shieh, K., Zhang, A. K., Alomair, B., & Wagner, D. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00149
LOOL: Low-Overhead, Optimization-Log-Guided Compiler Fuzzing
Berlakovich, F., Schwarcz, F., Barany, G., Mössenböck, H., & Brunthaler, S. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3807508
Nyzhnyk, A., & Kotliarov, O. Y. (2026) · Komp'ûternì sistemi ta merežì · doi:10.23939/csn2026.01.193
From coverage to causes: Data-centric fuzzing for Javascript engines
Ganguly, K. K., & Menzies, T. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108211
SemuFuzz: An efficient fuzzing approach for deep learning libraries based on semantic representation
Chen, Z., Chen, J., Cai, S., Chen, J., & Gu, W. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112999
pPatch: Automated Vulnerability Unpatching
Jing, T., Ding, P., Xu, M., Hu, Y., Yu, Z., & Mu, D. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3808201
Reality Check: Independent Evaluation of Modern Grey-Box Fuzzing Techniques
Frolikov, P. (2026) · IEEE Secure Development Conference (SecDev) · doi:10.1145/3805773.3806004
Böhme, M. (2026) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst69053.2026.00097
Self-citationSoK: A Systematic Review of Integration and Reproducibility of Fuzzing Research into AFL++
Schiller, N., Bars, N., Schloegel, M., & Holz, T. (2026) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp68448.2026.00074
FrameShift: Resizing Fuzzer Inputs Without Breaking Them
Green, H., Goues, C. L., & Brown, F. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3787765
Metadata
- Authors
- Böhme, M., Szekeres, L., & Metzman, J.
- Affiliations
- Monash University (AU)
- Google (United States) (US)
- Venue
- Proceedings of the 44th International Conference on Software Engineering
- Publisher
- ACM
- Published
- 2022-05-21
- Pages
- 1621-1633
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W4284706927
- Cited by
- 104
- References
- 47
- Citations by year
- 2026: 18 · 2025: 25 · 2024: 32 · 2023: 25 · 2022: 4
- FWCI
- 16.22
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (publishedVersion, other-oa)
- Funders
- École Polytechnique Fédérale de Lausanne
- Topics
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Software › Software Reliability and Analysis Research
- Keywords
- code coverage, fuzzing, software testing, bug detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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