Advertisement

The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning

  • Malte Isberner
  • Falk Howar
  • Bernhard Steffen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8734)

Abstract

In this paper we present TTT, a novel active automata learning algorithm formulated in the Minimally Adequate Teacher (MAT) framework. The distinguishing characteristic of TTT is its redundancy-free organization of observations, which can be exploited to achieve optimal (linear) space complexity. This is thanks to a thorough analysis of counterexamples, extracting and storing only the essential refining information. TTT is therefore particularly well-suited for application in a runtime verification context, where counterexamples (obtained, e.g., via monitoring) may be excessively long: as the execution time of a test sequence typically grows with its length, this would otherwise cause severe performance degradation. We illustrate the impact of TTT’s consequent redundancy-free approach along a number of examples.

Keywords

Model Check Finite Automaton Symbol Execution Membership Query Observation Table 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aarts, F., Heidarian, F., Kuppens, H., Olsen, P., Vaandrager, F.: Automata Learning through Counterexample Guided Abstraction Refinement. In: Giannakopoulou, D., Méry, D. (eds.) FM 2012. LNCS, vol. 7436, pp. 10–27. Springer, Heidelberg (2012), http://dx.doi.org/10.1007/978-3-642-32759-9_4 CrossRefGoogle Scholar
  2. 2.
    Angluin, D.: Learning Regular Sets from Queries and Counterexamples. Inf. Comput. 75(2), 87–106 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Balcázar, J.L., Díaz, J., Gavaldà, R.: Algorithms for Learning Finite Automata from Queries: A Unified View. In: Advances in Algorithms, Languages, and Complexity, pp. 53–72 (1997)Google Scholar
  4. 4.
    Berg, T., Jonsson, B., Leucker, M., Saksena, M.: Insights to Angluin’s Learning. Electron. Notes Theor. Comput. Sci. 118, 3–18 (2005), http://dx.doi.org/10.1016/j.entcs.2004.12.015 CrossRefGoogle Scholar
  5. 5.
    Bertolino, A., Calabrò, A., Merten, M., Steffen, B.: Never-Stop Learning: Continuous Validation of Learned Models for Evolving Systems through Monitoring. ERCIM News 2012(88) (2012)Google Scholar
  6. 6.
    Bollig, B., Habermehl, P., Kern, C., Leucker, M.: Angluin-style Learning of NFA. In: Proc. IJCAI 2009, San Francisco, CA, USA, pp. 1004–1009 (2009)Google Scholar
  7. 7.
    Broy, M., Jonsson, B., Katoen, J.-P., Leucker, M., Pretschner, A. (eds.): Model-Based Testing of Reactive Systems. LNCS, vol. 3472. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  8. 8.
    Cho, C.Y., Babić, D., Shin, R., Song, D.: Inference and Analysis of Formal Models of Botnet Command and Control Protocols. In: CCS 2010, pp. 426–440. ACM, Chicago (2010)Google Scholar
  9. 9.
    Choi, W., Necula, G., Sen, K.: Guided GUI Testing of Android Apps with Minimal Restart and Approximate Learning. In: Proc. OOPSLA 2013, pp. 623–640. ACM, New York (2013), http://doi.acm.org/10.1145/2509136.2509552 Google Scholar
  10. 10.
    Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press (1999)Google Scholar
  11. 11.
    Corbett, J., Dwyer, M., Hatcliff, J., Laubach, S., Pasareanu, C., Robby, Z.H.: Bandera: Extracting Finite-state Models from Java Source Code. In: Proc. Software Engineering, pp. 439–448 (2000)Google Scholar
  12. 12.
    De La Briandais, R.: File Searching Using Variable Length Keys. In: Western Joint Computer Conference, IRE-AIEE-ACM 1959, Western, pp. 295–298. ACM, New York (1959), http://doi.acm.org/10.1145/1457838.1457895 Google Scholar
  13. 13.
    Domaratzki, M., Kisman, D., Shallit, J.: On the Number of Distinct Languages Accepted by Finite Automata with n States. Journal of Automata, Languages and Combinatorics 7(4), 469–486 (2002)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Hagerer, A., Hungar, H.: Model generation by moderated regular extrapolation. In: Kutsche, R.-D., Weber, H. (eds.) FASE 2002. LNCS, vol. 2306, pp. 80–95. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Howar, F.: Active Learning of Interface Programs. Ph.D. thesis, TU Dortmund University (2012), http://dx.doi.org/2003/29486
  16. 16.
    Howar, F., Bauer, O., Merten, M., Steffen, B., Margaria, T.: The Teachers Crowd: The Impact of Distributed Oracles on Active Automata Learning. In: Hähnle, R., Knoop, J., Margaria, T., Schreiner, D., Steffen, B. (eds.) ISoLA 2011 Workshops 2011. CCIS, vol. 336, pp. 232–247. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    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
  18. 18.
    Irfan, M.N., Oriat, C., Groz, R.: Angluin Style Finite State Machine Inference with Non-optimal Counterexamples. In: 1st Int. Workshop on Model Inference in Testing (2010)Google Scholar
  19. 19.
    Isberner, M., Howar, F., Steffen, B.: Learning Register Automata: From Languages to Program Structures. Machine Learning 96(1-2), 65–98 (2014), http://dx.doi.org/10.1007/s10994-013-5419-7 MathSciNetCrossRefGoogle Scholar
  20. 20.
    Kearns, M.J., Vazirani, U.V.: An Introduction to Computational Learning Theory. MIT Press, Cambridge (1994)Google Scholar
  21. 21.
    Lorenzoli, D., Mariani, L., Pezzè, M.: Inferring State-based Behavior Models. In: Proc. WODA 2006, pp. 25–32. ACM, New York (2006)Google Scholar
  22. 22.
    Maler, O., Pnueli, A.: On the Learnability of Infinitary Regular Sets. Information and Computation 118(2), 316–326 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Margaria, T., Raffelt, H., Steffen, B.: Knowledge-based Relevance Filtering for Efficient System-level Test-based Model Generation. Innovations in Systems and Software Engineering 1(2), 147–156 (2005)CrossRefGoogle Scholar
  24. 24.
    Nerode, A.: Linear Automaton Transformations. Proceedings of the American Mathematical Society 9(4), 541–544 (1958)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Peled, D., Vardi, M.Y., Yannakakis, M.: Black Box Checking. In: Wu, J., Chanson, S.T., Gao, Q. (eds.) Proc. FORTE 1999, pp. 225–240. Kluwer Academic (1999)Google Scholar
  26. 26.
    Rivest, R.L., Schapire, R.E.: Inference of Finite Futomata Using Homing Sequences. Inf. Comput. 103(2), 299–347 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    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
  28. 28.
    Steffen, B., Howar, F., Merten, M.: Introduction to Active Automata Learning from a Practical Perspective. In: Bernardo, M., Issarny, V. (eds.) SFM 2011. LNCS, vol. 6659, pp. 256–296. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Malte Isberner
    • 1
  • Falk Howar
    • 2
  • Bernhard Steffen
    • 1
  1. 1.Dept. of Computer ScienceTU Dortmund UniversityDortmundGermany
  2. 2.Carnegie Mellon UniversityMoffett FieldUSA

Personalised recommendations