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Constraining Counterexamples in Hybrid System Falsification: Penalty-Based Approaches

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NASA Formal Methods (NFM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12229))

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Abstract

Falsification of hybrid systems is attracting ever-growing attention in quality assurance of Cyber-Physical Systems (CPS) as a practical alternative to exhaustive formal verification. In falsification, one searches for a falsifying input that drives a given black-box model to output an undesired signal. In this paper, we identify input constraints—such as the constraint “the throttle and brake pedals should not be pressed simultaneously” for an automotive powertrain model—as a key factor for the practical value of falsification methods. We propose three approaches for systematically addressing input constraints in optimization-based falsification, two among which come from the lexicographic method studied in the context of constrained multi-objective optimization. Our experiments show the approaches’ effectiveness.

The authors are supported by ERATO HASUO Metamathematics for Systems Design Project (No. JPMJER1603), JST; Zhenya Zhang is supported by Grant-in-Aid for JSPS Fellows No. 19J15218.

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Notes

  1. 1.

    Although the problem has a simple form of constraints, we prefer to name it unconstrained to distinguish it from the constrained setting we introduce later.

  2. 2.

    Note that, in general, it is not always possible to specify when an objective function is “achieved”. However, the lexicographic methods require that for functions \(f_1, \ldots , f_{N-1}\), this is possible, and this is applicable in our context.

  3. 3.

    Note that this is needed to distinguish inputs having robustness 0 (not falsifying) from those having negative robustness (falsifying).

  4. 4.

    Technically, we modified the fitness evaluation of Breach to use the 3 new fitness functions.

References

  1. List of references on constraint-handling techniques used with evolutionary algorithms. https://www.cs.cinvestav.mx/~constraint/

  2. Adimoolam, A., Dang, T., Donzé, A., Kapinski, J., Jin, X.: Classification and coverage-based falsification for embedded control systems. In: Majumdar, R., Kunčak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 483–503. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63387-9_24

    Chapter  Google Scholar 

  3. Akazaki, T., Hasuo, I.: Time robustness in MTL and expressivity in hybrid system falsification. In: Kroening, D., Păsăreanu, C.S. (eds.) CAV 2015. LNCS, vol. 9207, pp. 356–374. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21668-3_21

    Chapter  Google Scholar 

  4. Akazaki, T., Kumazawa, Y., Hasuo, I.: Causality-aided falsification. In: Proceedings First Workshop on Formal Verification of Autonomous Vehicles, FVAV@iFM 2017, Turin, Italy, 19th September 2017, vol. 257, pp. 3–18. EPTCS (2017)

    Google Scholar 

  5. Akazaki, T., Liu, S., Yamagata, Y., Duan, Y., Hao, J.: Falsification of cyber-physical systems using deep reinforcement learning. In: Havelund, K., Peleska, J., Roscoe, B., de Vink, E. (eds.) FM 2018. LNCS, vol. 10951, pp. 456–465. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95582-7_27

    Chapter  Google Scholar 

  6. Annapureddy, Y., Liu, C., Fainekos, G., Sankaranarayanan, S.: S-Taliro: a tool for temporal logic falsification for hybrid systems. In: Abdulla, P.A., Leino, K., Rustan, M. (eds.) TACAS 2011/ETAPS 2011, pp. 254–257. Springer-Verlag, Berlin, Heidelberg (2011)

    Google Scholar 

  7. Arcuri, A., Briand, L.: A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: Proceedings of the 33rd International Conference on Software Engineering, ICSE 2011, New York, NY, USA, pp. 1–10. ACM (2011)

    Google Scholar 

  8. Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2005, pp. 1769–1776. IEEE (2005)

    Google Scholar 

  9. Bäck, T., Hoffmeister, F., Schwefel, H.: A survey of evolution strategies. In: Belew, R.K., Booker, L.B., (eds.) Proceedings of the 4th International Conference on Genetic Algorithms, San Diego, CA, USA, July 1991, pp. 2–9 (1991)

    Google Scholar 

  10. Barbot, B., Basset, N., Dang, T.: Generation of signals under temporal constraints for CPS testing. In: Badger, J.M., Rozier, K.Y. (eds.) NASA Formal Methods. pp, pp. 54–70. Springer International Publishing, Cham (2019)

    Chapter  Google Scholar 

  11. Ben Abdessalem, R., Nejati, S., Briand, L.C., Stifter, T.: Testing vision-based control systems using learnable evolutionary algorithms. In: Proceedings of the 40th International Conference on Software Engineering, ICSE 2018, New York, NY, USA, pp. 1016–1026. ACM (2018)

    Google Scholar 

  12. Chang, K.-H.: Chapter 19 - multiobjective optimization and advanced topics. In: Chang, K.-H. (ed.) e-Design, pp. 1105–1173. Academic Press, Boston (2015)

    Chapter  Google Scholar 

  13. 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). https://doi.org/10.1007/978-3-319-24953-7_35

    Chapter  MATH  Google Scholar 

  14. Deshmukh, J.V., Horvat, M., Jin, X., Majumdar, R., Prabhu, V.S.: Testing cyber-physical systems through Bayesian optimization. ACM Trans. Embed. Comput. Syst. 16(5), 170:1–170:18 (2017)

    Google Scholar 

  15. 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). https://doi.org/10.1007/978-3-642-14295-6_17

    Chapter  Google Scholar 

  16. 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). https://doi.org/10.1007/978-3-642-15297-9_9

    Chapter  MATH  Google Scholar 

  17. 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). https://doi.org/10.1007/978-3-319-17524-9_10

    Chapter  Google Scholar 

  18. Dreossi, T., Donzé, A., Seshia, S.A.: Compositional falsification of cyber-physical systems with machine learning components. In: Bobaru, M., Havelund, K., Holzmann, G.J., Joshi, R. (eds.) NFM 2011. LNCS, vol. 6617. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20398-5

    Chapter  Google Scholar 

  19. Ehrgott, M.: Multicriteria Optimization. Springer-Verlag, Berlin (2005)

    MATH  Google Scholar 

  20. Ernst, G., Arcaini, P., Donzé, A., Fainekos, G., Mathesen, L., Pedrielli, G., Yaghoubi, S., Yamagata, Y., Zhang, Z.: ARCH-COMP 2019 category report: falsification. In: Frehse, G., Althoff, M., (eds.) ARCH19. 6th International Workshop on Applied Verification of Continuous and Hybrid Systems. EPiC Series in Computing, vol. 61, pp. 129–140. EasyChair (2019)

    Google Scholar 

  21. Ernst, G., Sedwards, S., Zhang, Z., Hasuo, I.: Fast falsification of hybrid systems using probabilistically adaptive input. In: Parker, D., Wolf, V. (eds.) Quantitative Evaluation of Systems. pp, pp. 165–181. Springer International Publishing, Cham (2019)

    Chapter  Google Scholar 

  22. Fainekos, G.E., Pappas, G.J.: Robustness of temporal logic specifications for continuous-time signals. Theor. Comput. Sci. 410(42), 4262–4291 (2009)

    Article  MathSciNet  Google Scholar 

  23. Fan, Z., Fang, Y., Li, W., Lu, J., Cai, X., Wei, C.: A comparative study of constrained multi-objective evolutionary algorithms on constrained multi-objective optimization problems. In: 2017 IEEE Congress on Evolutionary Computation, CEC 2017, pp. 209–216. IEEE (2017)

    Google Scholar 

  24. Ferrère, T., Nickovic, D., Donzé, A., Ito, H., Kapinski, J.: Interface-aware signal temporal logic. In: Ozay, N., Prabhakar, P., (eds.) Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control, HSCC 2019, Montreal, QC, Canada, 16–18 April 2019, pp. 57–66. ACM (2019)

    Google Scholar 

  25. Hoxha, B., Abbas, H., Fainekos, G.E.: Benchmarks for temporal logic requirements for automotive systems. In: Frehse, G., Althoff, M., (eds.) 1st and 2nd International Workshop on Applied veRification for Continuous and Hybrid Systems, ARCH@CPSWeek 2014, Berlin, Germany, 14 April 2014/ARCH@CPSWeek 2015, Seattle, USA, 13 April 2015. EPiC Series in Computing, vol. 34, pp. 25–30. EasyChair (2014)

    Google Scholar 

  26. Jin, X., Deshmukh, J.V., Kapinski, J., Ueda, K., Butts, K.: Powertrain control verification benchmark. In: Proceedings of the 17th International Conference on Hybrid Systems: Computation and Control, HSCC 2014, NY, USA, pp. 253–262. ACM (2014)

    Google Scholar 

  27. Kapinski, J., Deshmukh, J.V., Jin, X., Ito, H., Butts, K.: Simulation-based approaches for verification of embedded control systems: an overview of traditional and advanced modeling, testing, and verification techniques. IEEE Control Syst. 36(6), 45–64 (2016)

    Article  MathSciNet  Google Scholar 

  28. Kato, K., Ishikawa, F.: Learning-based falsification for model families of cyber-physical systems. In: 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), pp. 236–245, December 2019

    Google Scholar 

  29. Kuřátko, J., Ratschan, S.: Combined global and local search for the falsification of hybrid systems. In: Legay, A., Bozga, M. (eds.) FORMATS 2014. LNCS, vol. 8711, pp. 146–160. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10512-3_11

    Chapter  MATH  Google Scholar 

  30. Luersen, M.A., Le Riche, R.: Globalized Nelder-Mead method for engineering optimization. Comput. Struct. 82(23), 2251–2260 (2004)

    Article  Google Scholar 

  31. Nghiem, T., Sankaranarayanan, S., Fainekos, G., Ivancić, F., Gupta, A., Pappas, G.J.: Monte-carlo techniques for falsification of temporal properties of non-linear hybrid systems. In: Proceedings of the 13th ACM International Conference on Hybrid Systems: Computation and Control, HSCC 2010, NY, USA, pp. 211–220. ACM (2010)

    Google Scholar 

  32. Pinchera, D., Perna, S., Migliore, M.D.: A lexicographic approach for multi-objective optimization in antenna array design. Prog. Electromagn. Res. 59, 85–102 (2017)

    Article  Google Scholar 

  33. Silvetti, S., Policriti, A., Bortolussi, L.: An active learning approach to the falsification of black box cyber-physical systems. In: Polikarpova, N., Schneider, S. (eds.) IFM 2017. LNCS, vol. 10510, pp. 3–17. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66845-1_1

    Chapter  Google Scholar 

  34. Zhang, Z., Ernst, G., Sedwards, S., Arcaini, P., Hasuo, I.: Two-layered falsification of hybrid systems guided by monte carlo tree search. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 37(11), 2894–2905 (2018)

    Article  Google Scholar 

  35. Zutshi, A., Deshmukh, J.V., Sankaranarayanan, S., Kapinski, J.: Multiple shooting, cegar-based falsification for hybrid systems. In: 2014 International Conference on Embedded Software, EMSOFT 2014, New Delhi, India, 12–17 October 2014, pp. 5:1–5:10. ACM (2014)

    Google Scholar 

  36. Zutshi, A., Sankaranarayanan, S., Deshmukh, J.V., Kapinski, J., Jin, X.: Falsification of safety properties for closed loop control systems. In: Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control, HSCC 2015, Seattle, WA, USA, 14–16 April 2015, pp. 299–300 (2015)

    Google Scholar 

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Authors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Zhenya Zhang, Paolo Arcaini & Ichiro Hasuo

  2. SOKENDAI (The Graduate University for Advanced Studies), Hayama, Japan

    Zhenya Zhang & Ichiro Hasuo

  3. JSPS Research Fellow, Tokyo, Japan

    Zhenya Zhang

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  1. Zhenya Zhang
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Correspondence to Zhenya Zhang .

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  1. NASA Ames Research Center, Moffett Field, CA, USA

    Ritchie Lee

  2. SRI International, Menlo Park, CA, USA

    Susmit Jha

  3. KBR Inc., NASA Ames Research Center, Moffett Field, CA, USA

    Anastasia Mavridou

  4. NASA Ames Research Center, Moffett Field, CA, USA

    Dimitra Giannakopoulou

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Zhang, Z., Arcaini, P., Hasuo, I. (2020). Constraining Counterexamples in Hybrid System Falsification: Penalty-Based Approaches. In: Lee, R., Jha, S., Mavridou, A., Giannakopoulou, D. (eds) NASA Formal Methods. NFM 2020. Lecture Notes in Computer Science(), vol 12229. Springer, Cham. https://doi.org/10.1007/978-3-030-55754-6_24

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