  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.
