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A Passive Online Technique for Learning Hybrid Automata from Input/Output Traces

Published:29 October 2022Publication History
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Abstract

Specification synthesis is the process of deriving a model from the input-output traces of a system. It is used extensively in test design, reverse engineering, and system identification. One type of the resulting artifact of this process for cyber-physical systems is hybrid automata. They are intuitive, precise, tool independent, and at a high level of abstraction, and can model systems with both discrete and continuous variables. In this article, we propose a new technique for synthesizing hybrid automaton from the input-output traces of a non-linear cyber-physical system. Similarity detection in non-linear behaviors is the main challenge for extracting such models. We address this problem by utilizing the Dynamic Time Warping technique. Our approach is passive, meaning that it does not need interaction with the system during automata synthesis from the logged traces; and online, which means that each input/output trace is used only once in the procedure. In other words, each new trace can be used to improve the already synthesized automaton. We evaluated our algorithm in one industrial and two simulated case studies. The accuracy of the derived automata shows promising results.

REFERENCES

  1. [1] Aarts Fides, Jonsson Bengt, Uijen Johan, and Vaandrager Frits. 2015. Generating models of infinite-state communication protocols using regular inference with abstraction. Formal Methods Syst. Design 46, 1 (2015), 141.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Bellman Richard and Kalaba Robert. 1959. On adaptive control processes. IRE Trans. Autom. Control 4, 2 (1959), 19.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Blažič Sašo and Škrjanc Igor. 2020. Hybrid system identification by incremental fuzzy c-regression clustering. In Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ’20). IEEE, 17.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. [4] Choi Wontae. 2013. Automated testing of graphical user interfaces: A new algorithm and challenges. In Proceedings of the ACM Workshop on Mobile Development Lifecycle. 2728.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Choi Wontae, Necula George, and Sen Koushik. 2013. Guided gui testing of android apps with minimal restart and approximate learning. ACM Sigplan Notices 48, 10 (2013), 623640.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. [6] Esparza Javier, Leucker Martin, and Schlund Maximilian. 2011. Learning workflow petri nets. Fundamenta Informaticae 113, 3-4 (2011), 205228.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. [7] Fiterău-Broştean Paul, Janssen Ramon, and Vaandrager Frits. 2016. Combining model learning and model checking to analyze TCP implementations. In Proceedings of the International Conference on Computer Aided Verification. Springer, 454471.Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Gold E. Mark. 1978. Complexity of automaton identification from given data. Info. Control 37, 3 (1978), 302320.Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Henzinger Thomas A.. 2000. The theory of hybrid automata. In Verification of Digital and Hybrid Systems. Springer, 265292.Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Howar Falk and Steffen Bernhard. 2018. Active automata learning in practice. In Machine Learning for Dynamic Software Analysis: Potentials and Limits. Springer, 123148.Google ScholarGoogle Scholar
  11. [11] Jha Susmit, Tiwari Ashish, Seshia Sanjit A., Sahai Tuhin, and Shankar Natarajan. 2019. TeLEx: Learning signal temporal logic from positive examples using tightness metric. Formal Methods Syst. Design 54, 3 (2019), 364387.Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Jin Xiaoqing, Donzé Alexandre, Deshmukh Jyotirmoy V., and Seshia Sanjit A.. 2015. Mining requirements from closed-loop control models. IEEE Trans. Comput.-Aided Design Integr. Circ. Syst. 34, 11 (2015), 17041717.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] Juloski Aleksandar Lj, Heemels W. P. M. H., Ferrari-Trecate Giancarlo, Vidal René, Paoletti Simone, and Niessen J. H. G.. 2005. Comparison of four procedures for the identification of hybrid systems. In Proceedings of the International Workshop on Hybrid Systems: Computation and Control. Springer, 354369.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. [14] Kang Hong Jin and Lo David. 2021. Adversarial specification mining. ACM Trans. Softw. Eng. Methodol. 30, 2 (2021), 140.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Kianimajd A., Ruano M. G., Carvalho P., Henriques J., Rocha T., Paredes S., and Ruano A. E.. 2017. Comparison of different methods of measuring similarity in physiologic time series. Int. Fed. Autom. Control—Papers on Line 50, 1 (2017), 1100511010.Google ScholarGoogle Scholar
  16. [16] Kong Zhaodan, Jones Austin, Ayala Ana Medina, Gol Ebru Aydin, and Belta Calin. 2014. Temporal logic inference for classification and prediction from data. In Proceedings of the 17th International Conference on Hybrid Systems: Computation and Control. 273282.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Lamrani Imane, Banerjee Ayan, and Gupta Sandeep K. S.. 2018. HyMn: Mining linear hybrid automata from input output traces of cyber-physical systems. In Proceedings of the IEEE Industrial Cyber-Physical Systems (ICPS’18). IEEE, 264269.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Lauer Fabien and Bloch Gérard. 2019. Hybrid system identification. In Hybrid System Identification. Springer, 77101.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Le Tien-Duy B. and Lo David. 2018. Deep specification mining. In Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis. 106117.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] Maier Alexander. 2014. Online passive learning of timed automata for cyber-physical production systems. In Proceedings of the 12th IEEE International Conference on Industrial Informatics (INDIN’14). IEEE, 6066.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Medhat Ramy, Ramesh S., Bonakdarpour Borzoo, and Fischmeister Sebastian. 2015. A framework for mining hybrid automata from input/output traces. In Proceedings of the International Conference on Embedded Software (EMSOFT’15). IEEE, 177186.Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Okudono Takamasa, Waga Masaki, Sekiyama Taro, and Hasuo Ichiro. 2020. Weighted automata extraction from recurrent neural networks via regression on state spaces. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 53065314.Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Pillonetto Gianluigi. 2016. A new kernel-based approach to hybrid system identification. Automatica 70 (2016), 2131.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. [24] Raffelt Harald, Margaria Tiziana, Steffen Bernhard, and Merten Maik. 2008. Hybrid test of web applications with webtest. In Proceedings of the Workshop on Testing, Analysis, and Verification of Web Services and Applications. 17.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. [25] Senin Pavel. 2008. Dynamic time warping algorithm review. Information and Computer Science Department, University of Hawaii at Manoa Honolulu.Google ScholarGoogle Scholar
  26. [26] Soto Miriam García, Henzinger Thomas A., Schilling Christian, and Zeleznik Luka. 2019. Membership-based synthesis of linear hybrid automata. In Proceedings of the International Conference on Computer Aided Verification. Springer, 297314.Google ScholarGoogle ScholarCross RefCross Ref
  27. [27] Truong Charles, Oudre Laurent, and Vayatis Nicolas. 2020. Selective review of offline change point detection methods. Signal Process. 167 (2020), 107299.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. [28] Weiss Gail, Goldberg Yoav, and Yahav Eran. 2018. Extracting automata from recurrent neural networks using queries and counterexamples. In Proceedings of the International Conference on Machine Learning. PMLR, 52475256.Google ScholarGoogle Scholar
  29. [29] Zhang Nan, Yu Bin, Tian Cong, Duan Zhenhua, and Yuan Xiaoshuai. 2021. Temporal logic specification mining of programs. Theor. Comput. Sci. 857 (2021), 29–42.Google ScholarGoogle ScholarCross RefCross Ref

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        • Published in

          cover image ACM Transactions on Embedded Computing Systems
          ACM Transactions on Embedded Computing Systems  Volume 22, Issue 1
          January 2023
          512 pages
          ISSN:1539-9087
          EISSN:1558-3465
          DOI:10.1145/3567467
          • Editor:
          • Tulika Mitra
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 29 October 2022
          • Online AM: 16 August 2022
          • Accepted: 5 August 2022
          • Revised: 8 July 2022
          • Received: 14 October 2021
          Published in tecs Volume 22, Issue 1

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