Abstract
In active learning, an equivalence oracle is supposed to answer whether a hypothesis model is equivalent to the system under learning. Its implementation in real applications is considered a major bottleneck for active automata learning. The problem is especially difficult in the context of learning timed automata due to the infinitely large state space involved. In this paper, following the framework of combining mutation analysis and random testing, we propose an implementation of equivalence oracle in the context of learning deterministic one-clock timed automata (DOTAs). This includes two learning-friendly mutation operators, a heuristic test-case generation method, and a score-based test-case selection method. We implemented a prototype applying our approach by extending an existing tool on active learning of DOTAs and conducted extensive experiments. The results indicate that our method improves upon existing methods on the rate of learning correct models, the number of test cases required, and accumulated delay time in test cases.
Keywords
- Active learning
- Timed automata
- Model-based mutation testing
This work has been partially funded by NSFC under grant No. 61972284, 62032019, 62032024, 62192732, 62192730, and 61625206, by DFG project 389792660-TRR 248.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Set \(w=1\) if no additional information is known.
References
Aarts, F., Kuppens, H., Tretmans, J., Vaandrager, F., Verwer, S.: Improving active Mealy machine learning for protocol conformance testing. Mach. Learn. 96, 189–224 (2013). https://doi.org/10.1007/s10994-013-5405-0
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., Brandl, H., Jöbstl, E., Krenn, W., Schlick, R., Tiran, S.: Killing strategies for model-based mutation testing. Softw. Test. Verification Reliab. 25(8), 716–748 (2015). https://doi.org/10.1002/stvr.1522
Aichernig, B.K., Lorber, F., Ničković, D.: Time for mutants—model-based mutation testing with timed automata. In: Veanes, M., Viganò, L. (eds.) TAP 2013. LNCS, vol. 7942, pp. 20–38. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38916-0_2
Aichernig, B.K., Pferscher, A., Tappler, M.: From passive to active: learning timed automata efficiently. In: Lee, R., Jha, S., Mavridou, A., Giannakopoulou, D. (eds.) NFM 2020. LNCS, vol. 12229, pp. 1–19. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-55754-6_1
Aichernig, B.K., Tappler, M.: Efficient active automata learning via mutation testing. J. Autom. Reason. 63(4), 1103–1134 (2018). https://doi.org/10.1007/s10817-018-9486-0
Alur, R., Dill, D.L.: A theory of timed automata. Theoret. Comput. Sci. 126(2), 183–235 (1994). https://doi.org/10.1016/0304-3975(94)90010-8
An, J., Chen, M., Zhan, B., Zhan, N., Zhang, M.: Learning one-clock timed automata. In: TACAS 2020. LNCS, vol. 12078, pp. 444–462. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45190-5_25
An, J., Wang, L., Zhan, B., Zhan, N., Zhang, M.: Learning real-time automata. Sci. China Inf. Sci. 64(9), 1–17 (2021). https://doi.org/10.1007/s11432-019-2767-4
An, J., Zhan, B., Zhan, N., Zhang, M.: Learning nondeterministic real-time automata. ACM Trans. Embed. Comput. Syst. 20(5s), 1–26 (2021). https://doi.org/10.1145/3477030
Andrews, J.H., Briand, L.C., Labiche, Y., Namin, A.S.: Using mutation analysis for assessing and comparing testing coverage criteria. IEEE Trans. Software Eng. 32(8), 608–624 (2006). https://doi.org/10.1109/TSE.2006.83
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
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). https://doi.org/10.1007/978-3-540-31984-9_14
Chow, T.: Testing software design modeled by finite-state machines. IEEE Trans. Software Eng. 3, 178–187 (1978). https://doi.org/10.1109/TSE.1978.231496
En-Nouaary, A., Dssouli, R., Khendek, F.: Timed Wp-method: Testing real-time systems. IEEE Trans. Software Eng. 28(11), 1023–1038 (2002). https://doi.org/10.1109/TSE.2002.1049402
Grinchtein, O., Jonsson, B., Leucker, M.: Learning of event-recording automata. Theoret. Comput. Sci. 411(47), 4029–4054 (2010). https://doi.org/10.1016/j.tcs.2010.07.008
Henry, L., Jéron, T., Markey, N.: Active learning of timed automata with unobservable resets. In: Bertrand, N., Jansen, N. (eds.) FORMATS 2020. LNCS, vol. 12288, pp. 144–160. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57628-8_9
Howar, F., Jonsson, B., Vaandrager, F.: Combining black-box and white-box techniques for learning register automata. In: Steffen, B., Woeginger, G. (eds.) Computing and Software Science. LNCS, vol. 10000, pp. 563–588. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91908-9_26
Howar, F., Steffen, B., Merten, M.: From ZULU to RERS. In: Margaria, T., Steffen, B. (eds.) ISoLA 2010. LNCS, vol. 6415, pp. 687–704. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16558-0_55
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, Cham (2015). https://doi.org/10.1007/978-3-319-21690-4_32
Jia, Y., Harman, M.: An analysis and survey of the development of mutation testing. IEEE Trans. Software Eng. 37(5), 649–678 (2011). https://doi.org/10.1109/TSE.2010.62
Krichen, M., Tripakis, S.: Conformance testing for real-time systems. Formal Methods Syst. Des. 34(3), 238–304 (2009). https://doi.org/10.1007/s10703-009-0065-1
Larsen, K.G., Lorber, F., Nielsen, B., Nyman, U.: Mutation-based test-case generation with Ecdar. In: ICST Workshops 2017, pp. 319–328. IEEE (2017). https://doi.org/10.1109/ICSTW.2017.60
Maler, O., Mens, I.-E.: Learning regular languages over large alphabets. In: Ábrahám, E., Havelund, K. (eds.) TACAS 2014. LNCS, vol. 8413, pp. 485–499. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54862-8_41
Peled, D.A., Vardi, M.Y., Yannakakis, M.: Black box checking. J. Autom. Lang. Comb. 7(2), 225–246 (2002). https://doi.org/10.25596/jalc-2002-225
Trab, M.S.A., Counsell, S., Hierons, R.M.: Specification mutation analysis for validating timed testing approaches based on timed automata. In: COMPSAC 2012, pp. 660–669. IEEE Computer Society (2012). https://doi.org/10.1109/COMPSAC.2012.93
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
Vaandrager, F.: Model learning. Commun. ACM 60(2), 86–95 (2017). https://doi.org/10.1145/2967606
Vaandrager, F., Bloem, R., Ebrahimi, M.: Learning mealy machines with one timer. In: Leporati, A., Martín-Vide, C., Shapira, D., Zandron, C. (eds.) LATA 2021. LNCS, vol. 12638, pp. 157–170. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68195-1_13
Van Beek, D., Man, K., Reniers, M., Rooda, J., Schiffelers, R.: Syntax and semantics of timed Chi. J. Symb. Comput. JSC (2005)
Vega, J.J.O., Perrouin, G., Amrani, M., Schobbens, P.: Model-based mutation operators for timed systems: a taxonomy and research agenda. In: QRS 2018, pp. 325–332. IEEE (2018). https://doi.org/10.1109/QRS.2018.00045
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tang, X., Shen, W., Zhang, M., An, J., Zhan, B., Zhan, N. (2022). Learning Deterministic One-Clock Timed Automata via Mutation Testing. In: Bouajjani, A., Holík, L., Wu, Z. (eds) Automated Technology for Verification and Analysis. ATVA 2022. Lecture Notes in Computer Science, vol 13505. Springer, Cham. https://doi.org/10.1007/978-3-031-19992-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-19992-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19991-2
Online ISBN: 978-3-031-19992-9
eBook Packages: Computer ScienceComputer Science (R0)