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An Active Learning Approach to the Falsification of Black Box Cyber-Physical Systems

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Integrated Formal Methods (IFM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10510))

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Abstract

Search-based testing is widely used to find bugs in models of complex Cyber-Physical Systems. Latest research efforts have improved this approach by casting it as a falsification procedure of formally specified temporal properties, exploiting the robustness semantics of Signal Temporal Logic. The scaling of this approach to highly complex engineering systems requires efficient falsification procedures, which should be applicable also to black box models. Falsification is also exacerbated by the fact that inputs are often time-dependent functions. We tackle the falsification of formal properties of complex black box models of Cyber-Physical Systems, leveraging machine learning techniques from the area of Active Learning. Tailoring these techniques to the falsification problem with time-dependent, functional inputs, we show a considerable gain in computational effort, by reducing the number of model simulations needed. The effectiveness of the proposed approach is discussed on a challenging industrial-level benchmark from automotive.

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Notes

  1. 1.

    \(\phi \in \mathcal {L}\) iff \(\psi := \phi \,|\, \lnot \psi \,|\, \psi \vee \psi \,|\, \psi \wedge \psi \), with \(\phi \in \mathcal {L}_0\).

References

  1. Abbas, H., Fainekos, G., Sankaranarayanan, S., Ivančić, F., Gupta, A.: Probabilistic temporal logic falsification of cyber-physical systems. ACM Trans. Embed. Comput. Syst. (TECS) 12(2s), 95 (2013)

    Google Scholar 

  2. Akazaki, T.: Falsification of conditional safety properties for cyber-physical systems with Gaussian process regression. In: Falcone, Y., Sánchez, C. (eds.) RV 2016. LNCS, vol. 10012, pp. 439–446. Springer, Cham (2016). doi:10.1007/978-3-319-46982-9_27

    Chapter  Google Scholar 

  3. Alur, R., Courcoubetis, C., Halbwachs, N., Henzinger, T.A., Ho, P.-H., Nicollin, X., Olivero, A., Sifakis, J., Yovine, S.: The algorithmic analysis of hybrid systems. Theo. Comput. Sci. 138(1), 3–34 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  4. Annpureddy, Y., Liu, C., Fainekos, G., Sankaranarayanan, S.: S-TaLiRo: a tool for temporal logic falsification for hybrid systems. In: Abdulla, P.A., Leino, K.R.M. (eds.) TACAS 2011. LNCS, vol. 6605, pp. 254–257. Springer, Heidelberg (2011). doi:10.1007/978-3-642-19835-9_21

    Chapter  Google Scholar 

  5. Baheti, R., Gill, H.: Cyber-physical systems. Impact Control Technol. 12, 161–166 (2011)

    Google Scholar 

  6. Bardh Hoxha, H.A., Fainekos, G.: Benchmarks for temporal logic requirements for automotive systems. In: Proceedings of ARCH, vol. 34, pp. 25–30 (2015)

    Google Scholar 

  7. Deshmukh, J., Jin, X., Kapinski, J., Maler, O.: Stochastic local search for falsification of hybrid systems. In: Finkbeiner, B., Pu, G., Zhang, L. (eds.) ATVA 2015. LNCS, vol. 9364, pp. 500–517. Springer, Cham (2015). doi:10.1007/978-3-319-24953-7_35

    Chapter  Google Scholar 

  8. Donzé, A.: Breach, a toolbox for verification and parameter synthesis of hybrid systems. In: Touili, T., Cook, B., Jackson, P. (eds.) CAV 2010. LNCS, vol. 6174, pp. 167–170. Springer, Heidelberg (2010). doi:10.1007/978-3-642-14295-6_17

    Chapter  Google Scholar 

  9. Donzé, A., Maler, O.: Robust satisfaction of temporal logic over real-valued signals. In: Chatterjee, K., Henzinger, T.A. (eds.) FORMATS 2010. LNCS, vol. 6246, pp. 92–106. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15297-9_9

    Chapter  Google Scholar 

  10. Dreossi, T., Dang, T., Donzé, A., Kapinski, J., Jin, X., Deshmukh, J.V.: Efficient guiding strategies for testing of temporal properties of hybrid systems. In: Havelund, K., Holzmann, G., Joshi, R. (eds.) NFM 2015. LNCS, vol. 9058, pp. 127–142. Springer, Cham (2015). doi:10.1007/978-3-319-17524-9_10

    Google Scholar 

  11. Fainekos, G.E., Sankaranarayanan, S., Ueda, K., Yazarel, H.: Verification of automotive control applications using S-TaLiRo. In: Proceeings of ACC, pp. 3567–3572. IEEE (2012)

    Google Scholar 

  12. Jin, X., Deshmukh, J.V., Kapinski, J., Ueda, K., Butts, K.: Powertrain control verification benchmark. In: Proceedings of HSCC, pp. 253–262. ACM (2014)

    Google Scholar 

  13. Maler, O., Manna, Z., Pnueli, A.: Prom timed to hybrid systems. In: Bakker, J.W., Huizing, C., Roever, W.P., Rozenberg, G. (eds.) REX 1991. LNCS, vol. 600, pp. 447–484. Springer, Heidelberg (1992). doi:10.1007/BFb0032003

    Chapter  Google Scholar 

  14. Maler, O., Nickovic, D.: Monitoring temporal properties of continuous signals. In: Lakhnech, Y., Yovine, S. (eds.) FORMATS/FTRTFT -2004. LNCS, vol. 3253, pp. 152–166. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30206-3_12

    Chapter  Google Scholar 

  15. McKay, M.D., Beckman, R.J., Conover, W.J.: Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2), 239–245 (1979)

    MathSciNet  MATH  Google Scholar 

  16. Pnueli, A.: The temporal logic of programs. In: Proceedings of Foundations of Computer Science, pp. 46–57. IEEE (1977)

    Google Scholar 

  17. Rasmussen, C.E., Nickisch, H.: Gaussian processes for machine learning (GPML) toolbox. J. Mach. Learn. Res. 11, 3011–3015 (2010)

    MathSciNet  MATH  Google Scholar 

  18. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, New York (2006)

    MATH  Google Scholar 

  19. Rubinstein, R.Y., Kroese, D.P.: The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. Springer, New York (2013). doi:10.1007/978-1-4757-4321-0

    Book  MATH  Google Scholar 

  20. Sankaranarayananm S., Fainekos, G.: Falsification of temporal properties of hybrid systems using the cross-entropy method. In: Proceedings of HSCC, pp. 125–134. ACM (2012)

    Google Scholar 

  21. Vinnakota, B.: Analog and Mixed-Signal Test. Prentice Hall, Upper Saddle River (1998)

    Google Scholar 

  22. Zhao, Q., Krogh, B.H., Hubbard, P.: Generating test inputs for embedded control systems. IEEE Control Syst. 23(4), 49–57 (2003)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Esteco SpA, Trieste, Italy

    Simone Silvetti

  2. DIMA, University of Udine, Udine, Italy

    Simone Silvetti & Alberto Policriti

  3. Istituto di Genomica Applicata, Udine, Italy

    Alberto Policriti

  4. DMG, University of Trieste, Trieste, Italy

    Luca Bortolussi

  5. Modelling and Simulation Group, Saarland University, Saarbrücken, Germany

    Luca Bortolussi

  6. CNR-ISTI, Pisa, Italy

    Luca Bortolussi

Authors
  1. Simone Silvetti
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  2. Alberto Policriti
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  3. Luca Bortolussi
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Corresponding author

Correspondence to Simone Silvetti .

Editor information

Editors and Affiliations

  1. Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Nadia Polikarpova

  2. University of Surrey, Guildford, United Kingdom

    Steve Schneider

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Silvetti, S., Policriti, A., Bortolussi, L. (2017). An Active Learning Approach to the Falsification of Black Box Cyber-Physical Systems. In: Polikarpova, N., Schneider, S. (eds) Integrated Formal Methods. IFM 2017. Lecture Notes in Computer Science(), vol 10510. Springer, Cham. https://doi.org/10.1007/978-3-319-66845-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-66845-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66844-4

  • Online ISBN: 978-3-319-66845-1

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