Advertisement

Expressing Measurement Uncertainty in OCL/UML Datatypes

  • Manuel F. Bertoa
  • Nathalie Moreno
  • Gala Barquero
  • Loli Burgueño
  • Javier Troya
  • Antonio Vallecillo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10890)

Abstract

Uncertainty is an inherent property of any measure or estimation performed in any physical setting, and therefore it needs to be considered when modeling systems that manage real data. Although several modeling languages permit the representation of measurement uncertainty for describing certain system attributes, these aspects are not normally incorporated into their type systems. Thus, operating with uncertain values and propagating uncertainty are normally cumbersome processes, difficult to achieve at the model level. This paper proposes an extension of OCL and UML datatypes to incorporate data uncertainty coming from physical measurements or user estimations into the models, along with the set of operations defined for the values of these types.

Notes

Acknowledgements

This work has been partially supported by the Spanish Government under Grant TIN2014-52034-R. We would like to thank Martin Gogolla for his help and support during the development of the USE tool extension, and to the reviewers for their constructive comments and very valuable suggestions.

References

  1. 1.
    America, P.: Inheritance and subtyping in a parallel object-oriented language. In: Bézivin, J., Hullot, J.-M., Cointe, P., Lieberman, H. (eds.) ECOOP 1987. LNCS, vol. 276, pp. 234–242. Springer, Heidelberg (1987).  https://doi.org/10.1007/3-540-47891-4_22CrossRefGoogle Scholar
  2. 2.
    Bertoa, M.F., Moreno, N., Barquero, G., Burgueño, L., Troya, J., Vallecillo, A.: Uncertain OCL Datatypes, April 2018. http://atenea.lcc.uma.es/projects/UncertainOCLTypes.html
  3. 3.
    Broy, M.: Challenges in modeling cyber-physical systems. In: Proceedings of the ISPN 2013, pp. 5–6. IEEE (2013)Google Scholar
  4. 4.
    Büttner, F., Gogolla, M.: On OCL-based imperative languages. Sci. Comput. Program. 92, 162–178 (2014)CrossRefGoogle Scholar
  5. 5.
    Eramo, R., Pierantonio, A., Rosa, G.: Managing uncertainty in bidirectional model transformations. In: Proceedings of SLE 2015, pp. 49–58. ACM (2015)Google Scholar
  6. 6.
    Esfahani, N., Malek, S.: Uncertainty in self-adaptive software systems. In: de Lemos, R., Giese, H., Müller, H.A., Shaw, M. (eds.) Software Engineering for Self-Adaptive Systems II. LNCS, vol. 7475, pp. 214–238. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-35813-5_9CrossRefGoogle Scholar
  7. 7.
    Famelis, M., Salay, R., Chechik, M.: Partial models: towards modeling and reasoning with uncertainty. In: Proceedings of ICSE 2012, pp. 573–583. IEEE Press (2012)Google Scholar
  8. 8.
    Garlan, D.: Software engineering in an uncertain world. In: Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research (FoSER 2010), pp. 125–128. ACM (2010)Google Scholar
  9. 9.
    Gogolla, M., Büttner, F., Richters, M.: USE: a UML-based specification environment for validating UML and OCL. Sci. Comp. Prog. 69, 27–34 (2007)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Gogolla, M., Hilken, F.: Model validation and verification options in a contemporary UML and OCL analysis tool. In: Oberweis, A., Reussner, R. (eds.) Proceedings of the Modellierung (MODELLIERUNG 2016). LNI, vol. 254, pp. 203–218. GI (Gesellschaft für Informatik), Karlsruhe (2016)Google Scholar
  11. 11.
    Hall, B.D.: Component interfaces that support measurement uncertainty. Comput. Stand. Interfaces 28(3), 306–310 (2006)CrossRefGoogle Scholar
  12. 12.
    JCGM 100:2008: Evaluation of measurement data - Guide to the expression of uncertainty in measurement (GUM). Joint Committee for Guides in Metrology (2008). http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf
  13. 13.
    JCGM 101:2008: Evaluation of measurement data - Supplement 1 to the “Guide to the expression of uncertainty in measurement” - Propagation of distributions using a Monte Carlo method. Joint Committee for Guides in Metrology (2008). http://www.bipm.org/utils/common/documents/jcgm/JCGM_101_2008_E.pdf
  14. 14.
    JCGM 200:2012: International Vocabulary of Metrology - Basic and general concepts and associated terms (VIM), 3rd edn. Joint Committee for Guides in Metrology (2012). http://www.bipm.org/utils/common/documents/jcgm/JCGM_200_2012.pdf
  15. 15.
    Jiménez-Ramírez, A., Weber, B., Barba, I., del Valle, C.: Generating optimized configurable business process models in scenarios subject to uncertainty. Inf. Softw. Technol. 57, 571–594 (2015)CrossRefGoogle Scholar
  16. 16.
    Kosko, B.: Fuzziness vs. probability. Int. J. Gen. Syst. 17(2–3), 211–240 (1990)CrossRefGoogle Scholar
  17. 17.
    Lee, E.A.: Cyber physical systems: design challenges. In: Proceedings of ISORC 2008, pp. 363–369. IEEE (2008)Google Scholar
  18. 18.
    Liskov, B.H., Wing, J.M.: A behavioral notion of subtyping. ACM Trans. Program. Lang. Syst. 16(6), 1811–1841 (1994)CrossRefGoogle Scholar
  19. 19.
    Littlewood, B., Neil, M., Ostrolenk, G.: The role of models in managing the uncertainty of software-intensive systems. Reliab. Eng. Syst. Saf. 50(1), 87–95 (1995)CrossRefGoogle Scholar
  20. 20.
    Mayerhofer, T., Wimmer, M., Burgueño, L., Vallecillo, A.: Specifying quantities in software models (2018, submitted). Technical report: http://atenea.lcc.uma.es/index.php/Main_Page/Resources/DataUncertainty
  21. 21.
    Object Management Group: Object Constraint Language (OCL) Specification. Version 2.2, February 2010. OMG Document formal/2010-02-01Google Scholar
  22. 22.
    Object Management Group: UML Profile for MARTE: Modeling and Analysis of Real-Time Embedded Systems. Version 1.1, June 2011. OMG Document formal/2011-06-02Google Scholar
  23. 23.
    Object Management Group: Unified Modeling Language (UML) Specification. Version 2.5, March 2015. OMG Document formal/2015-03-01Google Scholar
  24. 24.
    Object Management Group: OMG Systems Modeling Language (SysML), Version 1.4, January 2016. OMG Document formal/2016-01-05Google Scholar
  25. 25.
    Object Management Group: Structured Metrics Metamodel (SMM) Specification. Version 1.1.1, April 2016. OMG Document formal/16-04-04Google Scholar
  26. 26.
    Salay, R., Chechik, M., Horkoff, J., Sandro, A.: Managing requirements uncertainty with partial models. Requir. Eng. 18(2), 107–128 (2013)CrossRefGoogle Scholar
  27. 27.
    Selic, B.: Beyond mere logic - a vision of modeling languages for the 21st century. In: Proceeding of MODELSWARD 2015 and PECCS 2015, p. IS–5. SciTePress (2015)Google Scholar
  28. 28.
    Vallecillo, A., Morcillo, C., Orue, P.: Expressing measurement uncertainty in software models. In: Proceedings of the 10th International Conference on the Quality of Information and Communications Technology (QUATIC), pp. 1–10 (2016)Google Scholar
  29. 29.
    Wikipedia: List of uncertainty propagation software. https://en.wikipedia.org/wiki/List_of_uncertainty_propagation_software. Accessed 13 Apr 2018
  30. 30.
    Wolf, M.: A modeling language for measurement uncertainty evaluation. Ph.D. thesis, ETH Zurich (2009)Google Scholar
  31. 31.
    Zhang, M., Ali, S., Yue, T., Norgren, R., Okariz, O.: Uncertainty-wise cyber-physical system test modeling. Softw. Syst. Model. (2017).  https://doi.org/10.1007/s10270-017-0609-6
  32. 32.
    Zhang, M., Selic, B., Ali, S., Yue, T., Okariz, O., Norgren, R.: Understanding uncertainty in cyber-physical systems: a conceptual model. In: Wąsowski, A., Lönn, H. (eds.) ECMFA 2016. LNCS, vol. 9764, pp. 247–264. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-42061-5_16CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Manuel F. Bertoa
    • 1
  • Nathalie Moreno
    • 1
  • Gala Barquero
    • 1
  • Loli Burgueño
    • 1
  • Javier Troya
    • 2
  • Antonio Vallecillo
    • 1
  1. 1.Universidad de MálagaMálagaSpain
  2. 2.Universidad de SevillaSevillaSpain

Personalised recommendations