Gray-Box Learning of Serial Compositions of Mealy Machines

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9690)

Abstract

We study the following gray-box learning problem: Given the serial composition of two Mealy machines A and B, where A is known and B is unknown, the goal is to learn a model of B using only output and equivalence queries on the composed machine.

We introduce an algorithm that solves this problem, using at most |B| equivalence queries, independently of the size of A. We discuss its efficient implementation and evaluate the algorithm on existing benchmark sets as well as randomly-generated machines.

References

  1. 1.
    Abel, A., Reineke, J.: MeMin: SAT-based exact minimization of incompletely specified mealy machines. In: ICCAD 2015. IEEE Press (2015)Google Scholar
  2. 2.
    Angluin, D.: Learning regular sets from queries and counter examples. Inf. Comput. 75(2), 87–106 (1987)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Babic, D., Botincan, M., Song, D.: Symbolic grey-box learning of input-output relations. Technical Report UCB/EECS-2012-59. University of California, Berkeley (2012)Google Scholar
  4. 4.
    Cobleigh, J.M., Giannakopoulou, D., Păsăreanu, C.S.: Learning assumptions for compositional verification. In: Garavel, H., Hatcliff, J. (eds.) TACAS 2003. LNCS, vol. 2619, pp. 331–346. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Elkind, E., Genest, B., Peled, D.A., Qu, H.: Grey-box checking. In: Najm, E., Pradat-Peyre, J.-F., Donzeau-Gouge, V.V. (eds.) FORTE 2006. LNCS, vol. 4229, pp. 420–435. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Grinchtein, O., Leucker, M.: Learning finite-state machines from inexperienced teachers. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds.) ICGI 2006. LNCS (LNAI), vol. 4201, pp. 344–345. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Grinchtein, O., Leucker, M., Piterman, N.: Inferring network invariants automatically. In: Furbach, U., Shankar, N. (eds.) IJCAR 2006. LNCS (LNAI), vol. 4130, pp. 483–497. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Groce, A., Peled, D.A., Yannakakis, M.: Adaptive model checking. In: Katoen, J.-P., Stevens, P. (eds.) TACAS 2002. LNCS, vol. 2280, pp. 357–370. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Henkler, S., et al.: Legacy component integration by the Fujaba real-time tool suite. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering, ICSE 2010, vol. 2. pp. 267–270. ACM, New York (2010)Google Scholar
  10. 10.
    Heule, M.J.H., Verwer, S.: Exact DFA identification using SAT solvers. In: Sempere, J.M., García, P. (eds.) ICGI 2010. LNCS, vol. 6339, pp. 66–79. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Hsu, Y., Lee, D.: Machine learning for implanted malicious code detection with incompletely specified system implementations. In: IEEE International Conference on Network Protocols, Washington, DC, USA, pp. 31–36 (2011)Google Scholar
  13. 13.
    Isberner, M., Howar, F., Steffen, B.: The open-source LearnLib. In: Kroening, D., Păsăreanu, C.S. (eds.) CAV 2015. LNCS, vol. 9206, pp. 487–495. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  14. 14.
    Lee, D.: Personal communication, January 2015Google Scholar
  15. 15.
    Leucker, M., Neider, D.: Learning minimal deterministic automata from inexperienced teachers. In: Margaria, T., Steffen, B. (eds.) ISoLA 2012, Part I. LNCS, vol. 7609, pp. 524–538. Springer, Heidelberg (2012)Google Scholar
  16. 16.
    Maler, O., Mens, I.-E.: Learning regular languages over large alphabets. In: Ábrahám, E., Havelund, K. (eds.) TACAS 2014 (ETAPS). LNCS, vol. 8413, pp. 485–499. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  17. 17.
    Pena, J., Oliveira, A.: A new algorithm for exact reduction of incompletely specified finite state machines. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 18(11), 1619–1632 (1999)CrossRefGoogle Scholar
  18. 18.
    Rivest, R.L., Schapire, R.E.: Inference of finite automata using homing sequences. Inf. Comput. 103(2), 299–347 (1993)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Shahbaz, M., Groz, R.: Inferring mealy machines. In: Cavalcanti, A., Dams, D.R. (eds.) FM 2009. LNCS, vol. 5850, pp. 207–222. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Vardhan, A., Sen, K., Viswanathan, M., Agha, G.: Using language inference to verify omega-regular properties. In: Halbwachs, N., Zuck, L.D. (eds.) TACAS 2005. LNCS, vol. 3440, pp. 45–60. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceSaarland UniversitySaarbückenGermany

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