Abstract
We are entering the age of learning systems! On the one hand, we are surrounded by devices that learn from our behavior [3]: household appliances, smart phones, wearables, cars, etc.—the most recent prominent example being Tesla Motor’s autopilot that learns from human drivers. On the other hand, man-made systems are becoming ever more complex, requiring us to learn the behavior of these systems: Learning-based testing [8, 13, 17], e.g., has been proposed as a method for testing the behavior of systems systematically without models and at a high level of abstraction. Promising results have been obtained here using active automata learning technology in verification [6, 16] and testing [1, 8]. At the same time, active automata learning has been extended to support the inference of program structures [5, 10] (it was first introduced for regular languages).
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References
Aarts, F., Kuppens, H., Tretmans, G.J., Vaandrager, F.W., Verwer, S.: Learning and testing the bounded retransmission protocol. In: Heinz, J., de la Higuera, C., Oates, T. (eds.) Proceedings of 11th International Conference on Grammatical Inference (ICGI 2012), 5–8 September 2012. JMLR Workshop and Conference Proceedings, vol. 21. pp. 4–18. University of Maryland, College Park (2012)
Bainczyk, A., Isberner, M., Margaria, T., Neubauer, J., Schieweck, A., Steffen, B.: ALEX: mixed-mode learning of web applications at ease. In: ISoLA 2016 (2016)
Bosch, J., Olsson, H.H.: Data-driven continuous evolution of smart systems. In: Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2016, pp. 28–34. ACM, New York (2016)
Cassel, S., Howar, F., Jonsson, B.: RALib: a LearnLib extension for inferring EFSMs. In: DIFTS 2015 at FMCAD 2015 (2015) (published online)
Cassel, S., Howar, F., Jonsson, B., Steffen, B.: Learning extended finite state machines. In: Giannakopoulou, D., Salaün, G. (eds.) SEFM 2014. LNCS, vol. 8702, pp. 250–264. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10431-7_18
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). doi:10.1007/3-540-36577-X_24
Giannakopoulou, D., Păsăreanu, C.S.: Learning techniques for software verification and validation – special track at ISoLA 2010. In: Margaria, T., Steffen, B. (eds.) ISoLA 2010, Part I. LNCS, vol. 6415, pp. 640–642. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16558-0_51
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). doi:10.1007/3-540-45923-5_6
Howar, F., Steffen, B.: Learning models for verification and testing — special track at ISoLA 2014 Track Introduction. In: Margaria, T., Steffen, B. (eds.) ISoLA 2014, Part I. LNCS, vol. 8802, pp. 199–201. Springer, Heidelberg (2014). doi:10.1007/978-3-662-45234-9_14
Isberner, M., Howar, F., Steffen, B.: Learning register automata: from languages to program structures. Mach. Learn. 96(1–2), 65–98 (2014)
Isberner, M., Howar, F., Steffen, B.: The open-source LearnLib. In: Kroening, D., Păsăreanu, C.S. (eds.) CAV 2015. LNCS, vol. 9206, pp. 487–495. Springer, Heidelberg (2015). doi:10.1007/978-3-319-21690-4_32
Mauritz, M., Howar, F., Rausch, A.: Assuring the safety of advanced driver assistance systems through a combination of simulation and runtime monitoring. In: ISoLA 2016 (2016)
Meinke, K., Sindhu, M.A.: Incremental learning-based testing for reactive systems. In: Gogolla, M., Wolff, B. (eds.) TAP 2011. LNCS, vol. 6706, pp. 134–151. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21768-5_11
Meinke, K., Sindhu, M.A.: Lbtest: a learning-based testing tool for reactive systems. In: Sixth IEEE International Conference on Software Testing, Verification and Validation, ICST 2013, Luxembourg, Luxembourg, 18–22 March 2013, pp. 447–454 (2013)
Pasareanu, C.S., Bobaru, M.: Learning techniques for software verification and validation. In: Margaria, T., Steffen, B. (eds.) ISoLA 2012, Part I. LNCS, vol. 7609, pp. 505–507. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34026-0_37
Peled, D., Vardi, M.Y., Yannakakis, M.: Black box checking. J. Automata Lang. Comb. 7(2), 225–246 (2002)
Raffelt, H., Merten, M., Steffen, B., Margaria, T.: Dynamic testing via automata learning. Int. J. Softw. Tools Technol. Transfer 11(4), 307–324 (2009)
Schudeleit, M., Zhang, M., Qi, X., Küçükay, F., Rausch, A.: Enhancement of an adaptive hev operating strategy using machine learning algorithms. In: ISoLA 2016 (2016)
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Howar, F., Meinke, K., Rausch, A. (2016). Learning Systems: Machine-Learning in Software Products and Learning-Based Analysis of Software Systems. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation: Discussion, Dissemination, Applications. ISoLA 2016. Lecture Notes in Computer Science(), vol 9953. Springer, Cham. https://doi.org/10.1007/978-3-319-47169-3_50
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