A Causality Analysis Framework for Component-Based Real-Time Systems

  • Shaohui Wang
  • Anaheed Ayoub
  • BaekGyu Kim
  • Gregor Gössler
  • Oleg Sokolsky
  • Insup Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8174)


We propose an approach to enhance the fault diagnosis in black-box component-based systems, in which only events on component interfaces are observable, and assume that causal dependencies between component interface events within components are not known. For such systems, we describe a causality analysis framework that helps us establish the causal relationship between component failures and system failures, given an observed system execution trace. The analysis is based on a formalization of counterfactual reasoning, and applicable to real-time systems. We illustrate the analysis with a case study from the medical device domain.


Fault Diagnosis System Failure System Property Causality Analysis Task Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alur, R., Feder, T., Henzinger, T.A.: The benefits of relaxing punctuality. J. ACM 43(1), 116–146 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Atmel Corporation. AT91SAM7S-EK Evaluation Board User Guide (2007),
  3. 3.
    Barry, R.: FreeRTOS User Manual,
  4. 4.
    Beer, I., Ben-David, S., Chockler, H., Orni, A., Trefler, R.: Explaining counterexamples using causality. In: Bouajjani, A., Maler, O. (eds.) CAV 2009. LNCS, vol. 5643, pp. 94–108. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Bhattacharyya, S., Huang, Z., Chandra, V., Kumar, R.: A discrete event systems approach to network fault management: detection and diagnosis of faults. In: American Control Conference, vol. 6, pp. 5108–5113 (2004)Google Scholar
  6. 6.
    de Kleer, J., Williams, B.C.: Diagnosing multiple faults. Artificial Intelligence 32(1), 97–130 (1987)CrossRefzbMATHGoogle Scholar
  7. 7.
    de Moura, L., Bjørner, N.: Z3: An efficient SMT solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Dubey, A., Karsai, G., Kereskenyi, R., Mahadevan, N.: Towards a real-time component framework for software health management. Technical Report ISIS-09-111, Vanderbilt University (2009)Google Scholar
  9. 9.
  10. 10.
    Generic PCA Infusion Pump Reference Implementation,
  11. 11.
    Gössler, G., Le Métayer, D., Raclet, J.-B.: Causality analysis in contract violation. In: Barringer, H., et al. (eds.) RV 2010. LNCS, vol. 6418, pp. 270–284. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Safety Requirements for the Generic PCA Pump,
  13. 13.
    Halpern, J.Y., Pearl, J.: Causes and Explanations: A Structural-Model Approach. Part I: Causes. The British Journal for the Philosophy of Science 56(4), 843–887 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Kuntz, M., Leitner-Fischer, F., Leue, S.: From probabilistic counterexamples via causality to fault trees. In: Flammini, F., Bologna, S., Vittorini, V. (eds.) SAFECOMP 2011. LNCS, vol. 6894, pp. 71–84. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Leitner-Fischer, F., Leue, S.: Causality checking for complex system models. Technical Report soft-12-02, University of Konstanz (2012)Google Scholar
  16. 16.
    Lewis, D.: Counterfactuals, 2nd edn. Wiley-Blackwell (2001)Google Scholar
  17. 17.
    Mahadevan, N., Abdelwahed, S., Dubey, A., Karsai, G.: Distributed diagnosis of complex systems using timed failure propagation graph models. In: The IEEE Systems Readiness Technology Conference, pp. 1–6 (2010)Google Scholar
  18. 18.
    Mendelson, E.: Introduction to Mathematical Logic, 4th edn. Chapman and Hall/CRC (1997)Google Scholar
  19. 19.
    Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press (2009)Google Scholar
  20. 20.
    Pnueli, A.: The temporal logic of programs. In: Proceedings of FOCS 1977, pp. 46–57 (1977)Google Scholar
  21. 21.
    Reiter, R.: A theory of diagnosis from first principles. Artificial Intelligence 32(1), 57–95 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Riegelman, R., et al.: Contributory cause: unnecessary and insufficient. Postgrad. Med. 66(2), 177 (1979)Google Scholar
  23. 23.
    Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., Teneketzis, D.: Failure diagnosis using discrete-event models. IEEE Transactions on Control Systems Technology 4(2), 105–124 (1996)CrossRefGoogle Scholar
  24. 24.
    Tian, J., Pearl, J.: Probabilities of causation: Bounds and identification. Annals of Mathematics and Artificial Intelligence 28, 287–313 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Tripakis, S.: A combined on-line/off-line framework for black-box fault diagnosis. In: Bensalem, S., Peled, D.A. (eds.) RV 2009. LNCS, vol. 5779, pp. 152–167. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  26. 26.
    Wang, S., Ayoub, A., Ivanov, R., Sokolsky, O., Lee, I.: Contract-based blame assignment by trace analysis. In: HiCoNS, pp. 117–125 (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shaohui Wang
    • 1
  • Anaheed Ayoub
    • 1
  • BaekGyu Kim
    • 1
  • Gregor Gössler
    • 2
  • Oleg Sokolsky
    • 1
  • Insup Lee
    • 1
  1. 1.Department of Computer and Information ScienceUniversity of PennsylvaniaUSA
  2. 2.INRIA GrenobleRhône-AlpesFrance

Personalised recommendations