Abstract
Model-based testing allows the creation of test cases from a model of the system under test. Often, such models are difficult to obtain, or even not available. Automata learning helps in inferring the model of a system by observing its behaviour. The model can be employed for many purposes, such as testing other implementations, regression testing, or model checking. We present an algorithm for active learning of nondeterministic, input-enabled, labelled transition systems, based on the well known Angluin’s L ⋆ algorithm. Under some assumptions, for dealing with nondeterminism, input-enabledness and equivalence checking, we prove that the algorithm produces a model whose behaviour is equivalent to the one under learning. We define new properties for the structure used in the algorithm, derived from the semantics of labelled transition systems. Such properties help the learning, by avoiding to query the system under learning when it is not necessary.
This research is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organisation for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs.Proofs available at http://www.italia.cs.ru.nl/publications/
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Aarts, F., Vaandrager, F.: Learning I/O automata. In: Gastin, P., Laroussinie, F. (eds.) CONCUR 2010. LNCS, vol. 6269, pp. 71–85. Springer, Heidelberg (2010)
Angluin, D.: Learning regular sets from queries and counterexamples. Information and Computation 75(2), 87–106 (1987)
Berg, T., Grinchtein, O., Jonsson, B., Leucker, M., Raffelt, H., Steffen, B.: On the correspondence between conformance testing and regular inference. In: Cerioli, M. (ed.) FASE 2005. LNCS, vol. 3442, pp. 175–189. Springer, Heidelberg (2005)
Bollig, B., Habermehl, P., Kern, C., Leucker, M.: Angluin-style learning of NFA, IJCAI 2009, pp. 1004–1009. Morgan Kaufmann Publishers Inc. (2009)
El-Fakih, K., Groz, R., Irfan, M.N., Shahbaz, M.: Learning finite state models of observable nondeterministic systems in a testing context. In: ICTSS 2010, pp. 97–102 (2010)
Howar, F., Isberner, M., Steffen, B., Bauer, O., Jonsson, B.: Inferring semantic interfaces of data structures. In: Margaria, T., Steffen, B. (eds.) ISoLA 2012, Part I. LNCS, vol. 7609, pp. 554–571. Springer, Heidelberg (2012)
Milner, R.: A Calculus of Communication Systems. LNCS, vol. 92. Springer, Heidelberg (1980)
Niese, O.: An integrated approach to testing complex systems. Ph.D. thesis, University of Dortmund (2003)
Pacharoen, W., Toshiaki, A., Bhattarakosol, P., Surarerks, A.: Active Learning of Non-deterministic Finite State Machines. In: Mathematical Problems in Engineering 2013, p. 11 (2013)
Rivest, R., Schapire, R.: Inference of finite automata using homing sequences. In: Hanson, S.J., Rivest, R.L., Remmele, W. (eds.) MIT-Siemens 1993. LNCS, vol. 661, pp. 51–73. Springer, Heidelberg (1993)
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)
Smeenk, W.: Applying Automata Learning to Complex Industrial Software. Radboud University Nijmegen, master’s thesis (2012)
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)
Tretmans, J.: Test Generation with Inputs, Outputs and Repetitive Quiescence. Software-Concepts and Tools 3, 103–120 (1996)
Tretmans, J.: Model-based testing and some steps towards test-based modelling. In: Bernardo, M., Issarny, V. (eds.) SFM 2011. LNCS, vol. 6659, pp. 297–326. Springer, Heidelberg (2011)
Volpato, M., Tretmans, J.: Towards quality of model-based testing in the ioco framework. In: Proceedings of the 2013 International Workshop on Joining AcadeMiA and Industry Contributions to Testing Automation, JAMAICA 2013, pp. 41–46. ACM, New York (2013)
Willemse, T.A.C.: Heuristics for ioco-based test-based modelling. In: Brim, L., Haverkort, B.R., Leucker, M., van de Pol, J. (eds.) FMICS 2006 and PDMC 2006. LNCS, vol. 4346, pp. 132–147. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Volpato, M., Tretmans, J. (2014). Active Learning of Nondeterministic Systems from an ioco Perspective. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change. ISoLA 2014. Lecture Notes in Computer Science, vol 8802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45234-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-662-45234-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45233-2
Online ISBN: 978-3-662-45234-9
eBook Packages: Computer ScienceComputer Science (R0)