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LearnLib: A Library for Automata Learning and Experimentation

  • Harald Raffelt
  • Bernhard Steffen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3922)

Abstract

In this tool demonstration we present the LearnLib, a library for automata learning and experimentation. Its modular structure allows users to configure their tailored learning scenarios, which exploit specific properties of the 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.

The demonstration of the LearnLib will include the extrapolation of a behavioral model for a realistic (legacy) system, and the statistical analysis of different variants of automata learning algorithms on the basis of random generated models.

References

  1. 1.
    Hagerer, A., Hungar, H., Niese, O., Steffen, B.: Model generation by moderated regular extrapolation. In: Kutsche, R.-D., Weber, H. (eds.) ETAPS 2002 and FASE 2002. LNCS, vol. 2306, pp. 80–95. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Cook, J.E., Wolf, A.L.: Discovering models of software processes from event-based data. ACM Transactions on Software Engineering and Methodology (TOSEM) 7, 215–249 (1998)CrossRefGoogle Scholar
  3. 3.
    Peled, D., Vardi, M.Y., Yannakakis, M.: Black box checking. In: Wu, J., Chanson, S.T., Gao, Q. (eds.) FORTE/PSTV 1999: Proc. of the Joint Int. Conference on Formal Description Techniques for Distributed System and Communication/Protocols and Protocol Specification, Testing and Verification, pp. 225–240. Kluwer Academic Publishers, Dordrecht (1999)Google Scholar
  4. 4.
    Brun, Y., Ernst, M.D.: Finding latent code errors via machine learning over program executions. In: ICSE 2004: Proc. of the 26th International Conference on Software Engineering, Edinburgh, Scotland, pp. 480–490 (2004)Google Scholar
  5. 5.
    Nimmer, J.W., Ernst, M.D.: Automatic generation of program specifications. In: ISSTA 2002: Proceedings of the 2002 International Symposium on Software Testing and Analysis, Rome, Italy, pp. 232–242 (2002)Google Scholar
  6. 6.
    Steffen, B., Hungar, H.: Behavior-based model construction. In: Zuck, L.D., Attie, P.C., Cortesi, A., Mukhopadhyay, S. (eds.) VMCAI 2003. LNCS, vol. 2575, pp. 5–19. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Steffen, B., Margaria, T., Raffelt, H., Niese, O.: Efficient test-based model generation of legacy systems. In: HLDVT 2004: Proc. of the 9th IEEE Int. Workshop on High Level Design Validation and Test, pp. 95–100. IEEE Computer Society Press, Sonoma (CA), USA (2004)Google Scholar
  8. 8.
    Raffelt, H., Steffen, B., Berg, T.: Learnlib: A library for automata learning and experimentation. In: Halbwachs, N., Zuck, L.D. (eds.) TACAS 2005. LNCS, vol. 3440, pp. 557–562. ACM Press, New York (2005)Google Scholar
  9. 9.
    Angluin, D.: Learning regular sets from queries and counterexamples. Information and Computation 2, 87–106 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Hungar, H., Niese, O., Steffen, B.: Domain-specific optimization in automata learning. In: Hunt Jr., W.A., Somenzi, F. (eds.) CAV 2003. LNCS, vol. 2725, pp. 315–327. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Margaria, T., Raffelt, H., Steffen, B.: Analyzing second-order effects between optimizations for system-level test-based model generation. In: ITC 2005: Proc. of IEEE International Test Conference (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Harald Raffelt
    • 1
  • Bernhard Steffen
    • 1
  1. 1.Chair of Programming SystemsUniversity of DortmundDortmundGermany

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