Abstract
We study a general class of distance metrics for deterministic Mealy machines. The metrics are induced by weight functions that specify the relative importance of input sequences. By choosing an appropriate weight function we may fine-tune a metric so that it captures some intuitive notion of quality. In particular, we present a metric that is based on the minimal number of inputs that must be provided to obtain a counterexample, starting from states that can be reached by a given set of logs. For any weight function, we may boost the performance of existing model learning algorithms by introducing an extra component, which we call the Comparator. Preliminary experiments show that use of the Comparator yields a significant reduction of the number of inputs required to learn correct models, compared to current state-of-the-art algorithms. In existing automata learning algorithms, the quality of subsequent hypotheses may decrease. Generalising a result of Smetsers et al., we show that the quality of hypotheses that are generated by the Comparator never decreases.
P. van den Bos—Supported by STW project 13859 (SUMBAT).
R. Smetsers—Supported by NWO project 628.001.009 (LEMMA).
F. Vaandrager—Supported by STW project 11763 (ITALIA).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
De Alfaro, L., Faella, M., Stoelinga, M.: Linear and branching system metrics. IEEE Trans. Software Eng. 35(2), 258–273 (2009)
Angluin, D.: Learning regular sets from queries and counterexamples. Inf. Comput. 75(2), 87–106 (1987)
De Bakker, J.W., Zucker, J.I.: Processes and the denotational semantics of concurrency. Inf. Control 54(12), 70–120 (1982)
Van den Bos, P.: Enhancing active automata learning by a user log based metric. Master’s thesis, Radboud University Nijmegen (2015)
Briones, L.B., Brinksma, E., Stoelinga, M.: A semantic framework for test coverage. In: Graf, S., Zhang, W. (eds.) ATVA 2006. LNCS, vol. 4218, pp. 399–414. Springer, Heidelberg (2006)
Černý, P., Henzinger, T.A., Radhakrishna, A.: Simulation distances. In: Gastin, P., Laroussinie, F. (eds.) CONCUR 2010. LNCS, vol. 6269, pp. 253–268. Springer, Heidelberg (2010)
de Alfaro, L., Henzinger, T.A., Majumdar, R.: Discounting the future in systems theory. In: Baeten, J.C.M., Lenstra, J.K., Parrow, J., Woeginger, G.J. (eds.) ICALP 2003. LNCS, vol. 2719, pp. 1022–1037. Springer, Heidelberg (2003)
de la Higuera, C.: Grammatical Inference. Cambridge University Press, Cambridge (2010)
de Ruiter, J., Poll, E.: Protocol state fuzzing of TLS implementations. In: USENIX Security 2015, pp. 193–206. USENIX Association, Washington, D.C., August 2015
Dijkstra, E.W.: The humble programmer. CACM 15(10), 859–866 (1972)
Droste, M., Kuich, W., Vogler, H.: Handbook of Weighted Automata, 1st edn. Springer, Heidelberg (2009)
Fiterău-Broştean, P., Janssen, R., Vaandrager, F.: Learning fragments of the TCP network protocol. In: Lang, F., Flammini, F. (eds.) FMICS 2014. LNCS, vol. 8718, pp. 78–93. Springer, Heidelberg (2014)
Fiterău-Broştean, P., Janssen, R., Vaandrager, F.: Combining model learning and model checking to analyze TCP implementations. Submitted to CAV (2016). http://www.sws.cs.ru.nl/publications/papers/fvaan/FJV16/
Henzinger, T.: Quantitative reactive modeling and verification. Comput. Sci. Res. Dev. 28(4), 331–344 (2013)
Isberner, M.: Foundations of Active Automata Learning: An Algorithmic Perspective. Ph.D. thesis, Technical University of Dortmund (2015)
Lee, D., Yannakakis, M.: Principles and methods of testing finite state machines-a survey. Proc. IEEE 84(8), 1090–1123 (1996)
Raffelt, H., Steffen, B., Berg, T., Margaria, T.: LearnLib: a framework for extrapolating behavioral models. 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)
Schuts, M., Hooman, J., Vaandrager, F.: Refactoring of legacy software using model learning and equivalence checking: an industrial experience report. In: Proceedings of iFM (2016)
Smeenk, W.: Applying automata learning to complex industrial software. Master’s thesis, Radboud University Nijmegen, September 2012
Smeenk, W., Moerman, J., Vaandrage, F., Jansen, D.N.: Applying Automata Learning to Embedded Control Software. In: Butler, M., Conchon, S., Zaïdi, F. (eds.) ICFEM 2015. LNCS, vol. 9407, pp. 67–83. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25423-4_5
Smetsers, R., Moerman, J., Jansen, D.N.: Minimal separating sequences for all Pairs of states. In: Dediu, A.-H., Janoušek, J., Martín-Vide, C., Truthe, B. (eds.) LATA 2016. LNCS, vol. 9618, pp. 181–193. Springer, Heidelberg (2016). doi:10.1007/978-3-319-30000-9_14
Smetsers, R., Volpato, M., Vaandrager, F., Verwer, S.: Bigger is not always better: on the quality of hypotheses in active automata learning. In: Proceedings of ICGI, JMLR: W&CP, vol. 34, pp. 167–181 (2014)
Sommerville, I.: Software Engineering. Addison-Wesley Publishing Company, Boston (2001)
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)
Thrane, C., Fahrenberg, U., Larsen, K.G.: Quantitative analysis of weighted transition systems. J. Logic Algebraic Program. 79(7), 689–703 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
van den Bos, P., Smetsers, R., Vaandrager, F. (2016). Enhancing Automata Learning by Log-Based Metrics. In: Ábrahám, E., Huisman, M. (eds) Integrated Formal Methods. IFM 2016. Lecture Notes in Computer Science(), vol 9681. Springer, Cham. https://doi.org/10.1007/978-3-319-33693-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-33693-0_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-33692-3
Online ISBN: 978-3-319-33693-0
eBook Packages: Computer ScienceComputer Science (R0)