  An ongoing challenge for learning algorithms formulated in the Minimally
Adequate Teacher framework is to efficiently obtain counterexamples. In this
paper we compare and combine conformance testing and mutation-based fuzzing
methods for obtaining counterexamples when learning finite state machine models
for the reactive software systems of the Rigorous Exampination of Reactive
Systems (RERS) challenge. We have found that for the LTL problems of the
challenge the fuzzer provided an independent confirmation that the learning
process had been successful, since no additional counterexamples were found.
For the reachability problems of the challenge, however, the fuzzer discovered
more reachable error states than the learner and tester, albeit in some cases
the learner and tester found some that were not discovered by the fuzzer. This
leads us to believe that these orthogonal approaches are complementary in the
context of model learning.
