Formal Methods for Aerospace Systems

Achievements and Challenges
  • Marco Bozzano
  • Harold Bruintjes
  • Alessandro Cimatti
  • Joost-Pieter Katoen
  • Thomas Noll
  • Stefano Tonetta
Chapter

Abstract

The size and complexity of control software in aerospace systems is rapidly increasing, and this development complicates its validation within the context of the overall spacecraft system. Classical validation methods are both labour intensive and error prone as they rely on manual analysis, review and inspection. Thus there is a growing trend to incorporate the use of automated formal methods. This chapter introduces the ESA-funded COMPASS project, which aims at an integrated system-software co-engineering approach focusing on a coherent set of specification and analysis techniques for evaluation of system-level correctness, safety, dependability and performability of on-board computer-based aerospace systems. Its modelling features and supporting toolset provide a unifying framework for system validation, employing state-of-the-art temporal-logic model checking techniques for infinite-state transition systems, both qualitative and probabilistic, with extensions to fault detection, identification and recovery (FDIR) and safety analysis. We provide an overview of the technology and of the results that have been achieved so far, and address several challenges for future developments. Current efforts of the project consortium concentrate on improving and advancing both process as well as technology of the COMPASS approach, with the goal of bringing the methods to higher levels of technology readiness.

Keywords

Safety and dependability analysis Performance analysis Model checking AADL modelling language 

References

  1. 1.
    S. Abdelwahed, G. Karsai, N. Mahadevan, S. Ofsthun, Practical implementation of diagnosis systems using timed failure propagation graph models. IEEE Trans. Instrum. Meas. 58(2), 240–247 (2009)CrossRefGoogle Scholar
  2. 2.
    E. Alaña, H. Naranjo, Y. Yushtein, M. Bozzano, A. Cimatti, M. Gario, R. de Ferluc, G. Garcia, Automated generation of FDIR for the COMPASS integrated toolset (AUTOGEF), in Proceedings of DASIA 2012, vol. ESA SP 701 (2012)Google Scholar
  3. 3.
    J. Alonso, M. Grottke, A.P. Nikora, K.S. Trivedi, An empirical investigation of fault repairs and mitigations in space mission system software, in Proceedings of DSN 2013 (IEEE, 2013), pp. 1–8Google Scholar
  4. 4.
    P. Anderson, Detecting bugs in safety-critical code. Dr. Dobb’s J. 33(3), 22–27 (2008), http://www.drdobbs.com/tools/detecting-bugs-in-safety-critical-code/206104422
  5. 5.
    M. Autili, L. Grunske, M. Lumpe, P. Pelliccione, A. Tang, Aligning qualitative, real-time, and probabilistic property specification patterns using a structured English grammar. IEEE Trans. Software Eng. 41(7), 620–638 (2015)CrossRefGoogle Scholar
  6. 6.
    C. Baier, B. Haverkort, H. Hermanns, J.P. Katoen, Model-checking algorithms for continuous-time Markov chains. IEEE Trans. Software Eng. 29(6), 524–541 (2003)CrossRefMATHGoogle Scholar
  7. 7.
    C. Baier, B.R. Haverkort, H. Hermanns, J.P. Katoen, Model checking meets performance evaluation. SIGMETRICS Perform. Eval. Rev. 32(4), 10–15 (2005)CrossRefMATHGoogle Scholar
  8. 8.
    C. Baier, J.P. Katoen, Principles of Model Checking (MIT Press, New York, 2008)MATHGoogle Scholar
  9. 9.
    E. Bartocci, R. Grosu, P. Katsaros, C.R. Ramakrishnan, S.A. Smolka, Model repair for probabilistic systems, in Proceedings of TACAS 2011. LNCS, vol. 6605 (Springer, 2011), pp. 326–340Google Scholar
  10. 10.
    A. Biere, A. Cimatti, E. Clarke, Y. Zhu, Symbolic model checking without BDDs, in Proceedings of TACAS 1999. LNCS, vol. 1579 (Springer, 1999), pp. 193–207Google Scholar
  11. 11.
    A. Biere, K. Heljanko, T.A. Junttila, T. Latvala, V. Schuppan, Linear encodings of bounded LTL model checking. Logical Methods Comput. Sci. 2(5) (2006)Google Scholar
  12. 12.
    B. Bittner, Formal failure analyses for effective fault management: an aerospace perspective, Ph.D. thesis, University of Trento, 2016Google Scholar
  13. 13.
    B. Bittner, M. Bozzano, R. Cavada, A. Cimatti, M. Gario, A. Griggio, C. Mattarei, A. Micheli, G. Zampedri, The xSAP safety analysis platform, in Proceedings of TACAS 2016. LNCS, vol. 9636 (Springer, 2016), pp. 533–539Google Scholar
  14. 14.
    B. Bittner, M. Bozzano, A. Cimatti, Automated synthesis of timed failure propagation graphs, in Proceedings of IJCAI 2016 (AAAI Press, 2016), pp. 972–978Google Scholar
  15. 15.
    B. Bittner, M. Bozzano, A. Cimatti, R. de Ferluc, M. Gario, A. Guiotto, Y. Yushtein, An integrated process for FDIR design in aerospace, in Proceedings of IMBSA 2014. LNCS, vol. 8822 (Springer, 2014), pp. 82–95Google Scholar
  16. 16.
    B. Bittner, M. Bozzano, A. Cimatti, X. Olive, Symbolic synthesis of observability requirements for diagnosability, in Proceedings of AAAI-12 (2012)Google Scholar
  17. 17.
    B. Bittner, M. Bozzano, A. Cimatti, G. Zampedri, Automated verification and tightening of failure propagation models, in Proceedings of AAAI 2016 (2016), pp. 3724–3730Google Scholar
  18. 18.
    V. Bos, H. Bruintjes, S. Tonetta, Catalogue of system and software properties, in Proceedings of SAFECOMP 2016. LNCS, vol. 9922 (Springer, 2016), pp. 88–101Google Scholar
  19. 19.
    H. Boudali, P. Crouzen, M. Stoelinga, A rigorous, compositional, and extensible framework for dynamic fault tree analysis. IEEE Trans. Dependable Secure Comput. 7(2), 128–143 (2010)CrossRefGoogle Scholar
  20. 20.
    M. Bozzano, R. Bruttomesso, A. Cimatti, T. Junttila, P. van Rossum, S. Schulz, R. Sebastiani, Mathsat: tight integration of SAT and mathematical decision procedures. J. Autom. Reason. 35, 265–293 (2005)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    M. Bozzano, A. Cimatti, M. Gario, A. Micheli, SMT-based validation of timed failure propagation graphs, in Proceedings of AAAI 2015 (2015), pp. 3724–3730Google Scholar
  22. 22.
    M. Bozzano, A. Cimatti, M. Gario, S. Tonetta, Formal design of fault detection and identification components using temporal epistemic logic, in Proceedings of TACAS 2014. LNCS, vol. 8413 (Springer, 2014), pp. 46–61Google Scholar
  23. 23.
    M. Bozzano, A. Cimatti, M. Gario, S. Tonetta, Formal design of asynchronous fault detection and identification components using temporal epistemic logic. Logical Methods Comput. Sci. 11(4), 1–33 (2015)MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    M. Bozzano, A. Cimatti, J.P. Katoen, P. Katsaros, K. Mokos, V.Y. Nguyen, T. Noll, B. Postma, M. Roveri, Spacecraft early design validation using formal methods. Reliab. Eng. Syst. Safety 132, 20–35 (2014)CrossRefGoogle Scholar
  25. 25.
    M. Bozzano, A. Cimatti, J.P. Katoen, V.Y. Nguyen, T. Noll, M. Roveri, Safety, dependability, and performance analysis of extended AADL models. Comput. J. 54(5), 754–775 (2011)CrossRefGoogle Scholar
  26. 26.
    M. Bozzano, A. Cimatti, C. Mattarei, A. Griggio, Efficient anytime techniques for model-based safety analysis, in Proceedings of CAV 2015. LNCS, vol. 9206 (Springer, 2015), pp. 603–621Google Scholar
  27. 27.
    M. Bozzano, A. Cimatti, C. Mattarei, S. Tonetta, Formal safety assessment via contract-based design, in Proceedings of ATVA 2014 (2014), pp. 81–97Google Scholar
  28. 28.
    M. Bozzano, A. Cimatti, F. Tapparo, Symbolic fault tree analysis for reactive systems, in Proceedings of ATVA 2007. LNCS, vol. 4762 (Springer, 2007), pp. 162–176Google Scholar
  29. 29.
    T. Brázdil, V. Forejt, J. Kretínský, A. Kucera, The satisfiability problem for Probabilistic CTL, in Proceedings of LICS 2008 (IEEE, 2008), pp. 391–402Google Scholar
  30. 30.
    M. Broy, B. Jonsson, J.P. Katoen, M. Leucker, A. Pretschner, (eds.), Model-Based Testing of Reactive Systems: Advanced Lectures. LNCS, Vol. 3472 (Springer, 2005)Google Scholar
  31. 31.
    H. Bruintjes, J.P. Katoen, D. Lesens, A statistical approach for timed reachability in AADL models, in Proceedings of DSN 2015 (IEEE CS Press, 2015), pp. 81–88Google Scholar
  32. 32.
    R. Cavada, A. Cimatti, M. Dorigatti, A. Griggio, A. Mariotti, A. Micheli, S. Mover, M. Roveri, S. Tonetta, The nuXmv symbolic model checker. CAV 2014, 334–342 (2014)Google Scholar
  33. 33.
    S. Chakraborty, J.P. Katoen, On the satisfiability of some simple probabilistic logics, in Proceedings of LICS 2016 (ACM, 2016), pp. 56–66Google Scholar
  34. 34.
    A. Cimatti, E. Clarke, E. Giunchiglia, F. Giunchiglia, M. Pistore, M. Roveri, R. Sebastiani, A. Tacchella, NuSMV 2: an open-source tool for symbolic model checking, in Proceedings of CAV 2002. LNCS, vol. 2404 (Springer, 2002), pp. 359–364Google Scholar
  35. 35.
    A. Cimatti, R. Demasi, S. Tonetta, Tightening a contract refinement, in Proceedings of SEFM 2016 (2016), pp. 386–402Google Scholar
  36. 36.
    A. Cimatti, M. Dorigatti, S. Tonetta, OCRA: a tool for checking the refinement of temporal contracts, in Proceedings of ASE 2013 (2013), pp. 702–705Google Scholar
  37. 37.
    A. Cimatti, C. Pecheur, R. Cavada, Formal verification of diagnosability via symbolic model checking, in Proceedings of IJCAI 2003 (Morgan Kaufmann, 2003), pp. 363–369Google Scholar
  38. 38.
    A. Cimatti, S. Tonetta, Contracts-refinement proof system for component-based embedded systems. Sci. Comput. Program. 97, 333–348 (2015)CrossRefGoogle Scholar
  39. 39.
    The COMPASS project, http://www.compass-toolset.org/
  40. 40.
    COMPASS user manual. Technical Report. Version 3.0, COMPASS Consortium (2016), http://www.compass-toolset.org/docs/compass-manual.pdf
  41. 41.
    COMPASS tutorial. Technical Report Version 3.0, COMPASS Consortium (2016), http://www.compass-toolset.org/docs/compass-tutorial.pdf
  42. 42.
    C. Dehnert, S. Junges, N. Jansen, F. Corzilius, M. Volk, H. Bruintjes, J.P. Katoen, E. Abraham, PROPhESY: a probabilistic parameter synthesis tool, in Proceedings of CAV 2015, LNCS, vol. 9206 (Springer, 2015), pp. 214–231Google Scholar
  43. 43.
    S. Derisavi, H. Hermanns, W.H. Sanders, Optimal state-space lumping in Markov chains. Inf. Process. Lett. 87(6), 309–315 (2003)MathSciNetCrossRefMATHGoogle Scholar
  44. 44.
    Software considerations in airborne systems and equipment certification. Software Standard DO-178C/ED-12C, RTCA Inc. and EUROCAE (2011)Google Scholar
  45. 45.
    J.B. Dugan, S.J. Bavuso, M.A. Boyd, Dynamic fault-tree models for fault-tolerant computer systems. IEEE Trans. Reliab. 41(3), 363–377 (1992)CrossRefMATHGoogle Scholar
  46. 46.
    M. Dwyer, G. Avrunin, J. Corbett, Patterns in property specifications for finite-state verification, in Proceedings of ICSE 1999 (IEEE CS Press, 1999), pp. 411–420Google Scholar
  47. 47.
    Space engineering: Verification. ECSS Standard E-ST-10-02C, European Cooperation for Space Standardization (2009)Google Scholar
  48. 48.
    Space engineering: System engineering general requirements. ECSS Standard E-ST-10C, European Cooperation for Space Standardization (2009)Google Scholar
  49. 49.
    Space product assurance: Failure modes, effects (and criticality) analysis (FMEA/FMECA). ECSS Standard Q-ST-30-02C, European Cooperation for Space Standardization (2009)Google Scholar
  50. 50.
    Space product assurance: Availability analysis. ECSS Standard Q-ST-30-09C, European Cooperation for Space Standardization (2008)Google Scholar
  51. 51.
    Space product assurance: Dependability. ECSS Standard Q-ST-30C, European Cooperation for Space Standardization (2009)Google Scholar
  52. 52.
    Space product assurance: Fault tree analysis—adoption notice ECSS/IEC 61025. ECSS Standard Q-ST-40-12C, European Cooperation for Space Standardization (2008)Google Scholar
  53. 53.
    Space product assurance: Safety. ECSS Standard Q-ST-40C, European Cooperation for Space Standardization (2009)Google Scholar
  54. 54.
    M.A. Esteve, J.P. Katoen, V.Y. Nguyen, B. Postma, Y. Yushtein, Formal correctness, safety, dependability and performance analysis of a satellite, in Proceedings of ICSE 2012 (ACM and IEEE CS Press, 2012), pp. 1022–1031Google Scholar
  55. 55.
    K. Etessami, M.Z. Kwiatkowska, M.Y. Vardi, M. Yannakakis, Multi-objective model checking of Markov decision processes. Logical Methods Comput. Sci. 4(4) (2008)Google Scholar
  56. 56.
    V. Forejt, M. Kwiatkowska, D. Parker, Pareto curves for probabilistic model checking, in Proceedings of ATVA 2012. LNCS, vol. 7561 (Springer, 2012), pp. 317–332Google Scholar
  57. 57.
    M. Gario, A formal foundation of FDI design via temporal epistemic logic. Ph.D. thesis, Trento University, Italy (2016), https://marco.gario.org/phd/gario_phd.pdf
  58. 58.
    D. Guck, T. Han, J.P. Katoen, M.R. Neuhäußer, Quantitative timed analysis of interactive Markov chains, in Proceedings of NFM 2012. LNCS, vol. 7226 (Springer, 2012), pp. 8–23Google Scholar
  59. 59.
    D. Guck, H. Hatefi, H. Hermanns, J.P. Katoen, M. Timmer, Modelling, reduction and analysis of Markov automata, in Proceedings of QEST 2013. LNCS, vol. 8054 (Springer, 2013), pp. 55–71Google Scholar
  60. 60.
    K. Heljanko, T.A. Junttila, T. Latvala, Incremental and complete bounded model checking for full PLTL, in Proceedings of CAV 2005. LNCS, vol. 3576 (2005), pp. 98–111Google Scholar
  61. 61.
    H. Hermanns, Interactive Markov Chains: The Quest for Quantified Quality. LNCS, vol. 2428 (Springer, 2002)Google Scholar
  62. 62.
    G.J. Holzmann, The power of 10: rules for developing safety-critical code. Computer 39(6), 95–99 (2006)CrossRefGoogle Scholar
  63. 63.
    N. Jansen, F. Corzilius, M. Volk, R. Wimmer, E. Abraham, J.P. Katoen, B. Becker, Accelerating parametric probabilistic verification, in Proceedings of QEST 2014. LNCS, vol. 8657 (Springer, 2014), pp. 404–420Google Scholar
  64. 64.
    S. Junges, D. Guck, J.P. Katoen, A. Rensink, M. Stoelinga, Fault trees on a diet, in Proceedings of SETTA 2015. LNCS, vol. 9409 (Springer, 2015), pp. 3–18Google Scholar
  65. 65.
    J.P. Katoen, V.Y. Nguyen, T. Noll, Formal validation methods in model-based spacecraft systems engineering, in Modeling and Simulation-Based Systems Engineering Handbook, Chap. 14 (CRC Press, 2014), pp. 339–375Google Scholar
  66. 66.
    J.P. Katoen, L. Song, L. Zhang, Probably safe or live, in Proceedings of CSL-LICS 2014 (ACM, 2014), pp. 55:1–55:10Google Scholar
  67. 67.
    J.P. Katoen, I.S. Zapreev, E.M. Hahn, H. Hermanns, D.N. Jansen, The ins and outs of the probabilistic model checker MRMC. Perform. Eval. 68(2), 90–104 (2011)CrossRefGoogle Scholar
  68. 68.
    M. Kwiatkowska, G. Norman, D. Parker, H. Qu, Compositional probabilistic verification through multi-objective model checking. Inf. Comput. 232, 38–65 (2013)MathSciNetCrossRefMATHGoogle Scholar
  69. 69.
  70. 70.
    A. Misra, J. Sztipanovits, A. Underbrink, R. Carnes, B. Purves, Diagnosability of dynamical systems, in 3rd International Workshop on Principles of Diagnosis (1992), pp. 239–244Google Scholar
  71. 71.
    MRMC – Markov Reward Model Checker, http://www.mrmc-tool.org/
  72. 72.
    T. Noll, Safety, dependability and performance analysis of aerospace systems, in Proceedings of FTSCS 2014. CCIS, vol. 476 (Springer, 2015), pp. 17–31Google Scholar
  73. 73.
    Nonelectronic parts reliability data (NPRD-2016). Technical Report, Quanterion Solutions Inc. (2015), https://www.quanterion.com/product/publications/nonelectronic-parts-reliability-data-publication-nprd-2016/
  74. 74.
    The NuSMV model checker, http://nusmv.fbk.eu
  75. 75.
    The nuXmv model checker, https://nuxmv.fbk.eu/
  76. 76.
    S.C. Ofsthun, S. Abdelwahed, Practical applications of timed failure propagation graphs for vehicle diagnosis, in Proceedings of Autotestcon 2007 (IEEE, 2007), pp. 250–259Google Scholar
  77. 77.
    S. Pathak, E. Abraham, N. Jansen, A. Tacchella, J.P. Katoen, A greedy approach for the efficient repair of stochastic models, in Proceedings of NFM 2015. LNCS, vol. 9058 (Springer, 2015), pp. 295–309Google Scholar
  78. 78.
    M. Perrotin, E. Conquet, J. Delange, A. Schiele, T. Tsiodras, TASTE: a real-time software engineering tool-chain overview, status, and future, in Proceedings of SDL 2011. LNCS, vol. 7083 (Springer, 2012), pp. 26–37Google Scholar
  79. 79.
    I. Pill, S. Semprini, R. Cavada, M. Roveri, R. Bloem, A. Cimatti, Formal analysis of hardware requirements, in Proceedings of DAC 2006 (2006), pp. 821–826Google Scholar
  80. 80.
    Reliability Prediction of Electronic Equipment. No. MIL-HDBK-217F in Military standardization handbook. Department of Defense, USA (1995), http://quicksearch.dla.mil/qsDocDetails.aspx?ident_number=53939
  81. 81.
    Architecture Analysis & Design Language (AADL) Annex, Volume 1, Annex E: Error Model Annex. SAE Standard AS5506/1A (International Society of Automotive Engineers, 2015)Google Scholar
  82. 82.
    Architecture Analysis and Design Language (AADL) Annex, Volume 1, Annex A: Graphical AADL Notation. SAE Standard AS5506/1 (International Society of Automotive Engineers, 2006)Google Scholar
  83. 83.
    Architecture Analysis & Design Language (AADL). SAE Standard AS5506 (International Society of Automotive Engineers, 2004)Google Scholar
  84. 84.
    Architecture Analysis & Design Language (AADL) (rev. B). SAE Standard AS5506B (International Society of Automotive Engineers, 2012)Google Scholar
  85. 85.
  86. 86.
    A. Valmari, G. Franceschinis, Simple \(O(m \log n)\) time Markov chain lumping, in Proceedings of TACAS 2010. LNCS, vol. 6015 (Springer, 2010), pp. 38–52Google Scholar
  87. 87.
    M. Volk, S. Junges, J.P. Katoen, Advancing dynamic fault tree analysis – get succinct state spaces fast and synthesise failure rates, in Proceedings of SAFECOMP 2016. LNCS, vol. 9922 (Springer, 2016), pp. 253–265Google Scholar
  88. 88.
    R. Wimmer, M. Herbstritt, H. Hermanns, K. Strampp, B. Becker, Sigref – a symbolic bisimulation tool box, in Proceedings of ATVA 2006. LNCS, vol. 4218 (Springer, 2006), pp. 477–492Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Marco Bozzano
    • 1
  • Harold Bruintjes
    • 2
  • Alessandro Cimatti
    • 1
  • Joost-Pieter Katoen
    • 2
  • Thomas Noll
    • 2
  • Stefano Tonetta
    • 1
  1. 1.Fondazione Bruno KesslerPovoItaly
  2. 2.Software Modeling and Verification GroupRWTH Aachen UniversityAachenGermany

Personalised recommendations