Abstract
A major hurdle for the application of automata learning to realistic systems is the identification of an adequate alphabet: it must be small enough, in particular finite, for the learning procedure to converge in reasonable time, and it must be expressive enough to describe the system at a level where its behavior is deterministic. In this paper, we combine our automated alphabet abstraction approach, which refines the global alphabet of the system to be learned on the fly during the learning process, with the principle of state-local alphabets: rather than determining a single global alphabet, we infer the optimal alphabet abstraction individually for each state. Our experimental results show that this does not only lead to an increased comprehensibility of the learned models, but also to a better performance of the learning process: indeed, besides the drastic – yet foreseeable – reduction in terms of membership queries, we also observed interesting cases where the number of equivalence queries was reduced.
This work was partially supported by the European Union FET Project CONNECT: Emergent Connectors for Eternal Software Intensive Networked Systems ( http://connect-forever.eu/ ).
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References
Aarts, F., Schmaltz, J., Vaandrager, F.: 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)
Angluin, D.: Learning Regular Sets from Queries and Counterexamples. Information and Computation 2(75), 87–106 (1987)
Gheorghiu Bobaru, M., Păsăreanu, C.S., Giannakopoulou, D.: Automated Assume-Guarantee Reasoning by Abstraction Refinement. In: Gupta, A., Malik, S. (eds.) CAV 2008. LNCS, vol. 5123, pp. 135–148. Springer, Heidelberg (2008)
Broy, M., Jonsson, B., Katoen, J.-P., Leucker, M., Pretschner, A. (eds.): Model-Based Testing of Reactive Systems. LNCS, vol. 3472. Springer, Heidelberg (2005)
Clarke, E., Grumberg, O., Jha, S., Lu, Y., Veith, H.: Counterexample-guided Abstraction Refinement for Symbolic Model Checking. J. ACM 50(5), 752–794 (2003)
Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. Springer (1999)
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)
Garavel, H., Lang, F., Mateescu, R., Serwe, W.: CADP 2010: A Toolbox for the Construction and Analysis of Distributed Processes. In: Abdulla, P.A., Leino, K.R.M. (eds.) TACAS 2011. LNCS, vol. 6605, pp. 372–387. Springer, Heidelberg (2011)
Gheorghiu, M., Giannakopoulou, D., Păsăreanu, C.S.: Refining Interface Alphabets for Compositional Verification. In: Grumberg, O., Huth, M. (eds.) TACAS 2007. LNCS, vol. 4424, pp. 292–307. Springer, Heidelberg (2007)
Giannakopoulou, D., Rakamarić, Z., Raman, V.: Symbolic Learning of Component Interfaces. In: Miné, A., Schmidt, D. (eds.) SAS 2012. LNCS, vol. 7460, pp. 248–264. Springer, Heidelberg (2012)
Hagerer, A., Hungar, H., Niese, O., Steffen, B.: Model Generation by Moderated Regular Extrapolation. In: Kutsche, R.-D., Weber, H. (eds.) FASE 2002. LNCS, vol. 2306, pp. 80–95. Springer, Heidelberg (2002)
Hagerer, A., Margaria, T., Niese, O., Steffen, B., Brune, G., Ide, H.-D.: Efficient Regression Testing of CTI-systems: Testing a Complex Call-center Solution. Annual Review of Comm., Int. Engineering Consortium (IEC) 55, 1033–1040 (2001)
Henzinger, T.A., Jhala, R., Majumdar, R., Sutre, G.: Lazy abstraction. In: POPL 2002, pp. 58–70. ACM, New York (2002)
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)
Howar, F., Steffen, B., Merten, M.: From ZULU to RERS: Lessons learned in the ZULU challenge. In: Margaria, T., Steffen, B. (eds.) ISoLA 2010, Part I. LNCS, vol. 6415, pp. 687–704. Springer, Heidelberg (2010)
Howar, F., Steffen, B., Merten, M.: Automata Learning with Automated Alphabet Abstraction Refinement. In: Jhala, R., Schmidt, D. (eds.) VMCAI 2011. LNCS, vol. 6538, pp. 263–277. Springer, Heidelberg (2011)
Issarny, V., Steffen, B., Jonsson, B., Blair, G.S., Grace, P., Kwiatkowska, M.Z., Calinescu, R., Inverardi, P., Tivoli, M., Bertolino, A., Sabetta, A.: CONNECT Challenges: Towards Emergent Connectors for Eternal Networked Systems. In: ICECCS, pp. 154–161 (2009)
Moller, F., Stevens, P.: Edinburgh Concurrency Workbench User Manual (Version 7.1), http://homepages.inf.ed.ac.uk/perdita/cwb/
Nerode, A.: Linear Automaton Transformations. Proceedings of the American Mathematical Society 9(4), 541–544 (1958)
Raffelt, H., Margaria, T., Steffen, B., Merten, M.: Hybrid Test of Web Applications with Webtest. In: TAV-WEB 2008, pp. 1–7. ACM, New York (2008)
Raffelt, H., Steffen, B., Berg, T., Margaria, T.: LearnLib: A Framework for Extrapolating Behavioral Models. International Journal on Software Tools for Technology Transfer (STTT) 11(5), 393–407 (2009)
Rivest, R.L., Schapire, R.E.: Inference of Finite Automata Using Homing Sequences. Inf. Comput. 103(2), 299–347 (1993)
Shahbaz, M., Groz, R.: Inferring Mealy Machines. In: Cavalcanti, A., Dams, D. (eds.) FM 2009. LNCS, vol. 5850, pp. 207–222. Springer, Heidelberg (2009)
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)
Steffen, B., Margaria, T., Raffelt, H., Niese, O.: Efficient test-based model generation of legacy systems. In: HLDVT 2004, Sonoma (CA), USA, pp. 95–100. IEEE Computer Society Press (November 2004)
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Isberner, M., Howar, F., Steffen, B. (2013). Inferring Automata with State-Local Alphabet Abstractions. In: Brat, G., Rungta, N., Venet, A. (eds) NASA Formal Methods. NFM 2013. Lecture Notes in Computer Science, vol 7871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38088-4_9
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