LearnLib: a framework for extrapolating behavioral models. In this paper, we present the LearnLib, a library of tools for automata learning, which is explicitly designed for the systematic experimental analysis of the profile of available learning algorithms and corresponding optimizations. Its modular structure allows users to configure their own tailored learning scenarios, which exploit specific properties of their envisioned applications. As has been shown earlier, exploiting application-specific structural features enables optimizations that may lead to performance gains of several orders of magnitude, a necessary precondition to make automata learning applicable to realistic scenarios.

References in zbMATH (referenced in 18 articles )

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  1. Kölbl, Martin; Leue, Stefan; Wies, Thomas: TarTar: a timed automata repair tool (2020)
  2. Aichernig, Bernhard K.; Tappler, Martin: Efficient active automata learning via mutation testing (2019)
  3. Autili, Marco; Inverardi, Paola; Spalazzese, Romina; Tivoli, Massimo; Mignosi, Filippo: Automated synthesis of application-layer connectors from automata-based specifications (2019)
  4. Fisman, Dana: Inferring regular languages and (\omega)-languages (2018)
  5. Li, Yong; Chen, Yu-Fang; Zhang, Lijun; Liu, Depeng: A novel learning algorithm for Büchi automata based on family of DFAs and classification trees (2017)
  6. Pinisetty, Srinivas; Preoteasa, Viorel; Tripakis, Stavros; Jéron, Thierry; Falcone, Yliès; Marchand, Hervé: Predictive runtime enforcement (2017)
  7. Cassel, Sofia; Howar, Falk; Jonsson, Bengt; Steffen, Bernhard: Active learning for extended finite state machines (2016)
  8. Mao, Hua; Chen, Yingke; Jaeger, Manfred; Nielsen, Thomas D.; Larsen, Kim G.; Nielsen, Brian: Learning deterministic probabilistic automata from a model checking perspective (2016)
  9. van den Bos, Petra; Smetsers, Rick; Vaandrager, Frits: Enhancing automata learning by log-based metrics (2016)
  10. Aarts, Fides; Jonsson, Bengt; Uijen, Johan; Vaandrager, Frits: Generating models of infinite-state communication protocols using regular inference with abstraction (2015)
  11. Aarts, Fides; Kuppens, Harco; Tretmans, Jan; Vaandrager, Frits; Verwer, Sicco: Improving active Mealy machine learning for protocol conformance testing (2014)
  12. Chen, Yu-Fang; Wang, Bow-Yaw: BULL: a library for learning algorithms of Boolean functions (2013) ioport
  13. Howar, Falk; Isberner, Malte; Merten, Maik; Steffen, Bernhard: Learnlib tutorial: From finite automata to register interface programs (2012) ioport
  14. Howar, Falk; Steffen, Bernhard; Merten, Maik: Automata learning with automated alphabet abstraction refinement (2011)
  15. de la Cámara, Pedro; del Mar Gallardo, María; Merino, Pedro; Sanán, David: Checking the reliability of socket based communication software (2009) ioport
  16. Raffelt, Harald; Merten, Maik; Steffen, Bernhard; Margaria, Tiziana: Dynamic testing via automata learning (2009) ioport
  17. Raffelt, Harald; Steffen, Bernhard; Berg, Therese; Margaria, Tiziana: LearnLib: a framework for extrapolating behavioral models (2009) ioport
  18. Raffelt, Harald; Steffen, Bernhard: LearnLib: A library for automata learning and experimentation (2006) ioport