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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.08077 |
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| _version_ | 1866914832849043456 |
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| author | Daniele, Cristian Andarzian, Seyed Behnam Poll, Erik |
| author_facet | Daniele, Cristian Andarzian, Seyed Behnam Poll, Erik |
| contents | This paper explores the use of active and passive learning, i.e.\ active and passive techniques to infer state machine models of systems, for fuzzing. Fuzzing has become a very popular and successful technique to improve the robustness of software over the past decade, but stateful systems are still difficult to fuzz. Passive and active techniques can help in a variety of ways: to compare and benchmark different fuzzers, to discover differences between various implementations of the same protocol, and to improve fuzzers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uses of Active and Passive Learning in Stateful Fuzzing Daniele, Cristian Andarzian, Seyed Behnam Poll, Erik Software Engineering This paper explores the use of active and passive learning, i.e.\ active and passive techniques to infer state machine models of systems, for fuzzing. Fuzzing has become a very popular and successful technique to improve the robustness of software over the past decade, but stateful systems are still difficult to fuzz. Passive and active techniques can help in a variety of ways: to compare and benchmark different fuzzers, to discover differences between various implementations of the same protocol, and to improve fuzzers. |
| title | Uses of Active and Passive Learning in Stateful Fuzzing |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2406.08077 |