Uses of Active and Passive Learning in Stateful Fuzzing
<|reference_start|>Uses of Active and Passive Learning in Stateful Fuzzing: 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.<|reference_end|>
@article{daniele2024uses,
    title={Uses of Active and Passive Learning in Stateful Fuzzing},
    author={Cristian Daniele, Seyed Behnam Andarzian, Erik Poll},
    journal={arXiv preprint arXiv:2406.08077},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08077},
    primaryClass={cs.SE}
}