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Active Learning of Extended Finite State Machines

  • Frits Vaandrager
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7641)

Abstract

Once they have high-level models of the behavior of software components, engineers can construct better software in less time. A key problem in practice, however, is the construction of models for existing software components, for which no or only limited documentation is available. In this talk, I will present an overview of recent work by my group — done in close collaboration with the Universities of Dortmund and Uppsala — in which we use machine learning to infer state diagram models of embedded controllers and network protocols fully automatically through observation and test, that is, through black box reverse engineering.

Keywords

Software Component Automaton Learning Reference Implementation Extended Finite State Machine Interface Automaton 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Aarts, F., Heidarian, F., Kuppens, H., Olsen, P., Vaandrager, F.W.: Automata Learning through Counterexample Guided Abstraction Refinement. In: Giannakopoulou, D., Méry, D. (eds.) FM 2012. LNCS, vol. 7436, pp. 10–27. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Aarts, F., Heidarian, F., Vaandrager, F.W.: A Theory of History Dependent Abstractions for Learning Interface Automata. In: Koutny, M., Ulidowski, I. (eds.) CONCUR 2012. LNCS, vol. 7454, pp. 240–255. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Aarts, F., Jonsson, B., Uijen, J.: Generating Models of Infinite-State Communication Protocols Using Regular Inference with Abstraction. In: Petrenko, A., Simão, A., Maldonado, J.C. (eds.) ICTSS 2010. LNCS, vol. 6435, pp. 188–204. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Aarts, F., Kuppens, H., Tretmans, G.J., Vaandrager, F.W., Verwer, S.: Learning and testing the bounded retransmission protocol. In: Heinz, J., de la Higuera, C., Oates, T. (eds.) Proceedings 11th International Conference on Grammatical Inference (ICGI 2012). JMLR Workshop and Conference Proceedings, September 5-8, vol. 21, pp. 4–18. University of Maryland, College Park (2012)Google Scholar
  5. 5.
    Aarts, F., Schmaltz, J., Vaandrager, F.W.: Inference and Abstraction of the Biometric Passport. In: Margaria, T., Steffen, B. (eds.) ISoLA 2010, Part I. LNCS, vol. 6415, pp. 673–686. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Angluin, D.: Learning regular sets from queries and counterexamples. Inf. Comput. 75(2), 87–106 (1987)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Helmink, L., Sellink, M.P.A., Vaandrager, F.W.: Proof-Checking a Data Link Protocol. In: Barendregt, H., Nipkow, T. (eds.) TYPES 1993. LNCS, vol. 806, pp. 127–165. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  8. 8.
    Howar, F., Steffen, B., Jonsson, B., Cassel, S.: Inferring Canonical Register Automata. In: Kuncak, V., Rybalchenko, A. (eds.) VMCAI 2012. LNCS, vol. 7148, pp. 251–266. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Merten, M., Howar, F., Steffen, B., Cassel, S., Jonsson, B.: Demonstrating Learning of Register Automata. In: Flanagan, C., König, B. (eds.) TACAS 2012. LNCS, vol. 7214, pp. 466–471. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Merten, M., Steffen, B., Howar, F., Margaria, T.: Next Generation LearnLib. In: Abdulla, P.A., Leino, K.R.M. (eds.) TACAS 2011. LNCS, vol. 6605, pp. 220–223. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Raffelt, H., Steffen, B., Berg, T., Margaria, T.: LearnLib: a framework for extrapolating behavioral models. STTT 11(5), 393–407 (2009)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Frits Vaandrager
    • 1
  1. 1.Institute for Computing and Information SciencesRadboud University NijmegenNijmegenThe Netherlands

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