Abstract
Active automata learning algorithms have gained increasing importance in the field of model-based system verification. For some classes of systems - especially deterministic systems, like Mealy machines, a variety of learning algorithm implementations is readily available. In this paper, we apply this technique to a measurement device from the automotive industry in order to systematically test its behaviour. However, our system under learning shows sparse non-deterministic behaviour, preventing the direct application of the available learning tools.
We propose an implementation of the active automata learning framework which masks this non-determinism. We repeat a previous model-based testing experiment with faulty devices and show that we can detect all injected faults. Most importantly, our technique was also able to find unknown bugs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Also known as membership query in literature.
- 2.
Including the equivalence oracle, since both components produce output queries.
- 3.
Simplified for the purpose of this work. In reality, the response to an S-command is either empty in the case of success or contains a specific error code.
- 4.
In the notation for labelled transition systems, \(\tau \) signifies an internal action which is not triggered via an external input. \(\tau \) and inputs can be arbitrarily interleaved.
signifies quiescence—i.e. the absence of an output. - 5.
In addition to the caching operations defined above, we also filter redundant output queries, as described by Margaria et al. [25].
References
Aarts, F., Jonsson, B., Uijen, J.: Generating models of infinite-state communication protocols using regular inference with abstraction. In: Petrenko, A., Simão, A., Maldonado, J.C. (eds.) ICTSS 2010. LNCS, vol. 6435, pp. 188–204. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16573-3_14
Aarts, F., de Ruiter, J., Poll, E.: Formal models of bank cards for free. In: Sixth IEEE International Conference on Software Testing, Verification and Validation, ICST 2013 Workshops Proceedings, Luxembourg, Luxembourg, 18–22 March 2013, pp. 461–468. IEEE (2013). https://doi.org/10.1109/ICSTW.2013.60
Aichernig, B.K.: Model-based mutation testing of reactive systems. In: Liu, Z., Woodcock, J., Zhu, H. (eds.) Theories of Programming and Formal Methods. LNCS, vol. 8051, pp. 23–36. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39698-4_2
Aichernig, B.K., et al.: Model-based mutation testing of an industrial measurement device. In: Seidl, M., Tillmann, N. (eds.) TAP 2014. LNCS, vol. 8570, pp. 1–19. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09099-3_1
Aichernig, B.K., Bloem, R., Ebrahimi, M., Tappler, M., Winter, J.: Automata learning for symbolic execution. In: 2018 Formal Methods in Computer Aided Design, FMCAD 2018, Austin, TX, USA, 30 October–2 November 2018. IEEE (2018). https://doi.org/10.23919/FMCAD.2018.8602991
Aichernig, B.K., Mostowski, W., Mousavi, M.R., Tappler, M., Taromirad, M.: Model learning and model-based testing. In: Bennaceur, A., Hähnle, R., Meinke, K. (eds.) Machine Learning for Dynamic Software Analysis: Potentials and Limits. LNCS, vol. 11026, pp. 74–100. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96562-8_3
Angluin, D.: Learning regular sets from queries and counterexamples. Inf. Comput. 75(2), 87–106 (1987). https://doi.org/10.1016/0890-5401(87)90052-6
AVL List GmbH: AVL Particle Counter - Product Guide, AT2858E, Rev. 08 (2013)
Berg, T., Jonsson, B., Leucker, M., Saksena, M.: Insights to Angluin’s learning. Electron. Notes Theoret. Comput. Sci. 118, 3–18 (2005). https://doi.org/10.1016/j.entcs.2004.12.015
Burghard, C.: Model-based testing of measurement devices using a domain-specific modelling language. Master’s thesis, Graz University of Technology, Institute of Software Technology (2018). http://truconf.ist.tugraz.at/wp-content/uploads/2018/04/MastersThesis_ChristianBurghard.pdf
Burghard, C., Stieglbauer, G., Korošec, R.: Introducing MDML - a domain-specific modelling language for automotive measurement devices. In: Joint Proceedings of the International Workshop on Quality Assurance in Computer Vision and the International Workshop on Digital Eco-Systems Co-Located with the 28th International Conference on Testing Software and Systems (ICTSS), pp. 28–31. CEUR-WS.org (2016). http://ceur-ws.org/Vol-1711/paperDECOSYS1.pdf
Chow, T.S.: Testing software design modeled by finite-state machines. IEEE Trans. Software Eng. 4(3), 178–187 (1978). https://doi.org/10.1109/TSE.1978.231496
Dias Neto, A.C., Subramanyan, R., Vieira, M., Travassos, G.H.: A survey on model-based testing approaches: a systematic review. In: Proceedings of the 1st ACM International Workshop on Empirical Assessment of Software Engineering Languages and Technologies: Held in Conjunction with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE) 2007, pp. 31–36. ACM (2007). https://dl.acm.org/citation.cfm?id=1353681
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, Cham (2014). https://doi.org/10.1007/978-3-319-10702-8_6
Fiterău-Broştean, P., Janssen, R., Vaandrager, F.: Combining model learning and model checking to analyze TCP implementations. In: Chaudhuri, S., Farzan, A. (eds.) CAV 2016. LNCS, vol. 9780, pp. 454–471. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41540-6_25
Fiterau-Brostean, P., Lenaerts, T., Poll, E., de Ruiter, J., Vaandrager, F.W., Verleg, P.: Model learning and model checking of SSH implementations. In: Proceedings of the 24th ACM SIGSOFT International SPIN Symposium on Model Checking of Software, Santa Barbara, CA, USA, 10–14 July 2017, pp. 142–151. ACM (2017). https://doi.org/10.1145/3092282.3092289
Gaudel, M.-C.: Testing can be formal, too. In: Mosses, P.D., Nielsen, M., Schwartzbach, M.I. (eds.) CAAP 1995. LNCS, vol. 915, pp. 82–96. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-59293-8_188
Howar, F., Steffen, B.: Active automata learning in practice. In: Bennaceur, A., Hähnle, R., Meinke, K. (eds.) Machine Learning for Dynamic Software Analysis: Potentials and Limits. LNCS, vol. 11026, pp. 123–148. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96562-8_5
Hungar, H., Niese, O., Steffen, B.: Domain-specific optimization in automata learning. In: Hunt, W.A., Somenzi, F. (eds.) CAV 2003. LNCS, vol. 2725, pp. 315–327. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45069-6_31
Isberner, M., Howar, F., Steffen, B.: The open-source LearnLib: A framework for active automata learning. In: Kroening, D., Păsăreanu, C.S. (eds.) CAV 2015. LNCS, vol. 9206, pp. 487–495. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21690-4_32
Jogun, K.: A universal interface for the integration of emissions testing equipment into engine testing automation systems: the VDA-AK SAMT-interface. Technical report, SAE Technical Paper (1994). https://doi.org/10.4271/940965
Jonsson, B., Vaandrager, F.W.: Learning Mealy machines with timers (2018). Preprint at http://www.sws.cs.ru.nl/publications/papers/fvaan/MMT/
Krenn, W., Schlick, R., Aichernig, B.K.: Mapping UML to labeled transition systems for test-case generation. In: de Boer, F.S., Bonsangue, M.M., Hallerstede, S., Leuschel, M. (eds.) FMCO 2009. LNCS, vol. 6286, pp. 186–207. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17071-3_10
Lee, D., Yannakakis, M.: Principles and methods of testing finite state machines - a survey. Proc. IEEE 84(8), 1090–1123 (1996). https://doi.org/10.1109/5.533956
Margaria, T., Raffelt, H., Steffen, B.: Knowledge-based relevance filtering for efficient system-level test-based model generation. Innovations Syst. Softw. Eng. 1(2), 147–156 (2005). https://doi.org/10.1007/s11334-005-0016-y
Niese, O.: An integrated approach to testing complex systems. Ph.D. thesis, Technical University of Dortmund, Germany (2003). https://doi.org/10.17877/DE290R-14871
Offutt, A.J., Voas, J.M.: Subsumption of condition coverage techniques by mutation testing. Technical report, George Madison University, Fairfax, VA, USA (1996). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.83.8904&rep=rep1&type=pdf
OMG: OMG Unified Modeling Language (OMG UML), Version 2.5.1. Object Management Group, August 2017. http://www.omg.org/spec/UML/2.5.1
de Ruiter, J., Poll, E.: Protocol state fuzzing of TLS implementations. In: 24th USENIX Security Symposium, USENIX Security 15, Washington, D.C., USA, 12–14 August 2015, pp. 193–206. USENIX Association (2015). https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/de-ruiter
McMahon Stone, C., Chothia, T., de Ruiter, J.: Extending automated protocol state learning for the 802.11 4-way handshake. In: Lopez, J., Zhou, J., Soriano, M. (eds.) ESORICS 2018. LNCS, vol. 11098, pp. 325–345. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99073-6_16
Tappler, M., Aichernig, B.K., Bloem, R.: Model-based testing IoT communication via active automata learning. In: 2017 IEEE International Conference on Software Testing, Verification and Validation, ICST 2017, Tokyo, Japan, 13–17 March 2017, pp. 276–287. IEEE (2017). https://doi.org/10.1109/ICST.2017.32
Utting, M., Pretschner, A., Legeard, B.: A taxonomy of model-based testing approaches. Softw. Test. Verification Reliab. 22(5), 297–312 (2012). https://doi.org/10.1002/stvr.456
Acknowledgements
Part of this work was supported by the TU Graz LEAD project “Dependable Internet of Things in Adverse Environments”. We thank the four anonymous reviewers and Martin Tappler for their valuable feedback.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Aichernig, B.K., Burghard, C., Korošec, R. (2019). Learning-Based Testing of an Industrial Measurement Device. In: Badger, J., Rozier, K. (eds) NASA Formal Methods. NFM 2019. Lecture Notes in Computer Science(), vol 11460. Springer, Cham. https://doi.org/10.1007/978-3-030-20652-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-20652-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20651-2
Online ISBN: 978-3-030-20652-9
eBook Packages: Computer ScienceComputer Science (R0)