Regular Model Checking Using Solver Technologies and Automata Learning

  • Daniel Neider
  • Nils Jansen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7871)

Abstract

Regular Model Checking is a popular verification technique where large and even infinite sets of program configurations can be encoded symbolically by finite automata. Thereby, the handling of regular sets of initial and bad configurations often imposes a serious restriction in practical applications. We present two new algorithms both utilizing modern solver technologies and automata learning. The first one works in a CEGAR-like fashion by iteratively refining an abstraction of the reachable state space using counterexamples, while the second one is based on Angluin’s prominent learning algorithm. We show the feasibility and competitiveness of our approaches on different benchmarks and compare them to other established tools.

References

  1. 1.
    Clarke Jr., E.M., Grumberg, O., Peled, D.A.: Model Checking. The MIT Press (1999)Google Scholar
  2. 2.
    Baier, C., Katoen, J.P.: Principles of Model Checking. The MIT Press (2008)Google Scholar
  3. 3.
    Bouajjani, A., Jonsson, B., Nilsson, M., Touili, T.: Regular model checking. In: Emerson, E.A., Sistla, A.P. (eds.) CAV 2000. LNCS, vol. 1855, pp. 403–418. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Wolper, P., Boigelot, B.: Verifying systems with infinite but regular state spaces. In: Vardi, M.Y. (ed.) CAV 1998. LNCS, vol. 1427, pp. 88–97. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Legay, A.: T(O)RMC: A tool for (ω)-regular model checking. In: Gupta, A., Malik, S. (eds.) CAV 2008. LNCS, vol. 5123, pp. 548–551. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Bardin, S., Finkel, A., Leroux, J.: FASTer acceleration of counter automata in practice. In: Jensen, K., Podelski, A. (eds.) TACAS 2004. LNCS, vol. 2988, pp. 576–590. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Vardhan, A., Viswanathan, M.: LEVER: A tool for learning based verification. In: Ball, T., Jones, R.B. (eds.) CAV 2006. LNCS, vol. 4144, pp. 471–474. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Neider, D.: Computing minimal separating DFAs and regular invariants using SAT and SMT solvers. In: Chakraborty, S., Mukund, M. (eds.) ATVA 2012. LNCS, vol. 7561, pp. 354–369. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Angluin, D.: Learning regular sets from queries and counterexamples. Inf. Comput. 75(2), 87–106 (1987)MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Clarke, E., Grumberg, O., Jha, S., Lu, Y., Veith, H.: Counterexample-guided abstraction refinement. In: Emerson, E.A., Sistla, A.P. (eds.) CAV 2000. LNCS, vol. 1855, pp. 154–169. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Gold, E.M.: Complexity of automaton identification from given data. Information and Control 37(3), 302–320 (1978)MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Daniel Neider
    • 1
  • Nils Jansen
    • 1
  1. 1.RWTH Aachen UniversityGermany

Personalised recommendations