• Corpus ID: 16191377

Complementing Model Learning with Mutation-Based Fuzzing

  title={Complementing Model Learning with Mutation-Based Fuzzing},
  author={Rick Smetsers and Joshua Moerman and Mark Janssen and Sicco Verwer},
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… 

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