Skip to main content
Springer Nature Link
Log in

U-Map: A Reference Map for Safe Handling of Runtime Uncertainties

  • Conference paper
  • First Online:
Model-Based Safety and Assessment (IMBSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12297))

Included in the following conference series:

  • 1140 Accesses

Abstract

“Uncertainty is certain” – a well-stablished fact that challenges design and engineering of dynamic systems. Cyber Physical Systems (CPSs) must function and perform tasks safely in real world contexts that might not be engineered specifically for them. These dynamic contexts are often accompanied by the pervasive presence of uncertainty. The dynamic nature of such systems as well as their ever-growing complexity further complicate safety assurance and require a paradigm shift towards more effective runtime safety assurance techniques. Many of the present runtime safety assurance techniques consider certain dynamic aspects of the system and its context, but not the uncertainty aspects completely. This paper presents results from an ongoing research to effectively handle runtime uncertainties in a model-based approach to assure system safety. In this paper, we propose a reference map called Uncertainty Map (U-Map) that can be used during system design to handle runtime uncertainties and apply it to a case study. The U-Map consists of an exhaustive set of possible uncertainties that are mapped to sets of potentially resulting hazards as well as possible runtime mitigation measures. It is intended to facilitate the identification of uncertainty-induced hazards during early design and contribute to the safe handling of runtime uncertainties.

The work leading to this paper was partially funded by the German Federal Ministry of Education and Research under grant number 01IS16043 Collaborative Embedded Systems (CrESt).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Anderson, E.L., Hattis, D.: A. uncertainty and variability. Risk Analysis 19(1), 47–49 (1999)

    Google Scholar 

  2. Ben-Gal, I.: Bayesian networks. In: Encyclopedia of Statistics in Quality and Reliability, vol. 1. Springer, Dordrecht (2008). https://doi.org/10.1007/978-1-4020-6754-9_1624

  3. Blair, G., Bencomo, N., France, R.B.: Models@ run. time. Computer 42(10), 22–27 (2009)

    Article  Google Scholar 

  4. Brun, Y., et al.: Engineering self-adaptive systems through feedback loops. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 48–70. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02161-9_3

    Chapter  Google Scholar 

  5. Cámara, J., Garlan, D., Kang, W.G., Peng, W., Schmerl, B.: Uncertainty in self-adaptive systems: Categories, management, and perspectives. Carnegie-Mellon Univ Pittsburg PA United States, Technical report (2017)

    Google Scholar 

  6. Cámara, J., Moreno, G., Garlan, D.: Reasoning about human participation in self-adaptive systems. In: 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp. 146–156. IEEE (2015)

    Google Scholar 

  7. Cheng, B.H.C., et al.: Software engineering for self-adaptive systems: a research roadmap. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 1–26. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02161-9_1

    Chapter  Google Scholar 

  8. Cheng, S.W., Garlan, D.: Handling uncertainty in autonomic systems. In: International Workshop on Living with Uncertainty (2007)

    Google Scholar 

  9. Elkhodary, A., Esfahani, N., Malek, S.: Fusion: a framework for engineering self-tuning self-adaptive software systems. In: Proceedings of the Eighteenth ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 7–16 (2010)

    Google Scholar 

  10. Esfahani, N., Kouroshfar, E., Malek, S.: Taming uncertainty in self-adaptive software. In: Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, pp. 234–244 (2011)

    Google Scholar 

  11. Garlan, D.: Software engineering in an uncertain world. In: Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research, pp. 125–128 (2010)

    Google Scholar 

  12. Garlan, D., Cheng, S.W., Huang, A.C., Schmerl, B., Steenkiste, P.: Rainbow: architecture-based self-adaptation with reusable infrastructure. Computer 37(10), 46–54 (2004)

    Article  Google Scholar 

  13. Giese, H., et al.: Living with uncertainty in the age of runtime models. In: Bencomo, N., France, R., Cheng, B.H.C., Aßmann, U. (eds.) Models@run.time. LNCS, vol. 8378, pp. 47–100. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08915-7_3

    Chapter  Google Scholar 

  14. Hildebrandt, C., Bandyszak, T., Petrovska, A., Laxman, N., Cioroaica, E., Törsleff, S.: EURECA: epistemic uncertainty classification scheme for runtime information exchange in collaborative system groups. SICS Software-Intens. Cyber-Phys. Syst. 34(4), 177–190 (2019)

    Google Scholar 

  15. Hoffman, F.O., Hammonds, J.S.: Propagation of uncertainty in risk assessments: the need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability. Risk Anal. 14(5), 707–712 (1994)

    Article  Google Scholar 

  16. International Organization for Standardization (ISO): ISO 12100: Safety of machinery-General principles for design-Risk assessment and risk reduction (2010)

    Google Scholar 

  17. Kabir, S., et al.: A runtime safety analysis concept for open adaptive systems. In: Papadopoulos, Y., Aslansefat, K., Katsaros, P., Bozzano, M. (eds.) IMBSA 2019. LNCS, vol. 11842, pp. 332–346. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32872-6_22

    Chapter  Google Scholar 

  18. Kanal, L.N., Lemmer, J.F.: Uncertainty in Artificial Intelligence. Elsevier, Amsterdam (2014)

    MATH  Google Scholar 

  19. Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)

    Article  MathSciNet  Google Scholar 

  20. Kolmogorov, A.N., Bharucha-Reid, A.T.: Foundations of the Theory of Probability: Second, English edn. Courier Dover Publications, Mineola (2018)

    Google Scholar 

  21. Koo, C.H., Laxman, N., Möhrle, F.: Runtime safety analysis for reconfigurable production systems. In: The 30th European Safety and Reliability Conference (ESREL). Research Publishing, Singapore (2020, in press)

    Google Scholar 

  22. Koopman, P., Osyk, B., Weast, J.: Autonomous vehicles meet the physical world: RSS, variability, uncertainty, and proving safety. In: Romanovsky, A., Troubitsyna, E., Bitsch, F. (eds.) SAFECOMP 2019. LNCS, vol. 11698, pp. 245–253. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26601-1_17

    Chapter  Google Scholar 

  23. Laffont, J.J.: Economie de l’incertain et de l’information. MIT Press, Cambridge (1989)

    Google Scholar 

  24. Mahdavi-Hezavehi, S., Avgeriou, P., Weyns, D.: A classification framework of uncertainty in architecture-based self-adaptive systems with multiple quality requirements. In: Managing Trade-Offs in Adaptable Software Architectures, pp. 45–77. Elsevier (2017)

    Google Scholar 

  25. Östberg, K., Bengtsson, M.: Run time safety analysis for automotive systems in an open and adaptive environment (2013)

    Google Scholar 

  26. Perez-Palacin, D., Mirandola, R.: Uncertainties in the modeling of self-adaptive systems: a taxonomy and an example of availability evaluation. In: Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering, pp. 3–14 (2014)

    Google Scholar 

  27. Ramirez, A.J., Jensen, A.C., Cheng, B.H.: A taxonomy of uncertainty for dynamically adaptive systems. In: 2012 7th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp. 99–108. IEEE (2012)

    Google Scholar 

  28. Rowe, W.D.: Understanding uncertainty. Risk Anal. 14(5), 743–750 (1994)

    Article  Google Scholar 

  29. Saffiotti, A.: Handling uncertainty in control of autonomous robots. In: Hunter, A., Parsons, S. (eds.) Applications of Uncertainty Formalisms. LNCS (LNAI), vol. 1455, pp. 198–224. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-49426-X_10

    Chapter  Google Scholar 

  30. Schneider, D., Becker, M., Trapp, M.: Approaching runtime trust assurance in open adaptive systems. In: Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp. 196–201. Association for Computing Machinery, New York (2011)

    Google Scholar 

  31. Schneider, D., Trapp, M., Papadopoulos, Y., Armengaud, E., Zeller, M., Höfig, K.: WAP: digital dependability identities. In: 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE), pp. 324–329. IEEE (2015)

    Google Scholar 

  32. Staab, S., Studer, R.: Handbook on Ontologies. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-540-92673-3

    Book  MATH  Google Scholar 

  33. Walker, W.E., et al.: Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrat. Assess. 4(1), 5–17 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Software Engineering: Dependability, Technische Universität Kaiserslautern, Kaiserslautern, Germany

    Nishanth Laxman & Peter Liggesmeyer

  2. Corporate Sector Research and Advanced Engineering, Robert Bosch GmbH, Renningen, Germany

    Chee Hung Koo

Authors
  1. Nishanth Laxman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chee Hung Koo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Liggesmeyer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nishanth Laxman .

Editor information

Editors and Affiliations

  1. Siemens AG, Munich, Bayern, Germany

    Marc Zeller

  2. University of Applied Sciences Rosenheim, Rosenheim, Germany

    Kai Höfig

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Laxman, N., Koo, C.H., Liggesmeyer, P. (2020). U-Map: A Reference Map for Safe Handling of Runtime Uncertainties. In: Zeller, M., Höfig, K. (eds) Model-Based Safety and Assessment. IMBSA 2020. Lecture Notes in Computer Science(), vol 12297. Springer, Cham. https://doi.org/10.1007/978-3-030-58920-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58920-2_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58919-6

  • Online ISBN: 978-3-030-58920-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics