Hostname: page-component-848d4c4894-nmvwc Total loading time: 0 Render date: 2024-06-19T15:36:51.816Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling uncertain requirements

Published online by Cambridge University Press:  16 May 2024

Lukas Block
Lukas Block*
Affiliation:
Fraunhofer IAO, Germany
*
lukas.block@iao.fraunhofer.de

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Anticipating all technical requirements that a product must meet throughout its lifespan has become difficult due to a rise in market, regulatory, and technological uncertainty. As a result, the attribute values of these requirements may be highly uncertain at the start of product development. We propose a mathematical model that captures and quantifies this uncertainty in a clear and comprehensive manner. We evaluate the approach by encoding uncertain requirements for an automotive project. Misconceptions regarding probabilities are alleviated and the requirements are unambiguously defined.

Keywords

requirements managementuncertaintymodelling
Type
Design Methods and Tools
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 503 - 512
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Abdelmadjid, L. and Mimoun, M. (2022), “Uncertain Decision-Making Requirements Formalizing with Complement Fuzzy UML Model”, Procedia Computer Science, Vol. 198, pp. 317–322. https://doi.org/10.1016/j.procs.2021.12.247.CrossRefGoogle Scholar
Beck, J.L. (2010), “Bayesian system identification based on probability logic”, Structural Control and Health Monitoring, Vol. 17 No. 7, pp. 825–847. https://doi.org/10.1002/stc.424.CrossRefGoogle Scholar
Beer, M. (2009), “Fuzzy Probability Theory”, in Meyers, R.A. (Ed.), Encyclopedia of complexity and systems science: With 420 tables, Springer reference, Springer, New York, NY, pp. 4047–4060. https://doi.org/10.1007/978-0-387-30440-3_237.CrossRefGoogle Scholar
Beierle, C. and Kern-Isberner, G. (2019), Methoden wissensbasierter Systeme: Grundlagen, Algorithmen, Anwendungen, Computational Intelligence, 6th ed., Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-27084-1.Google Scholar
Block, L. (2023), Ein Verfahren zur Entwicklung flexibler Fahrzeug-Software- und -Hardware-Architekturen unter Unsicherheit, Springer Nature, Wiesbaden. https://doi.org/10.1007/978-3-658-42804-4.CrossRefGoogle Scholar
Block, L., Binz, H. and Roth, D. (2021), “Extrapolation of Objectives in Product Development under Uncertainty”, in Binz, H., Bertsche, B., Spath, D. and Roth, D. (Eds.), Stuttgarter Symposium für Produktentwicklung: SSP 2021, 20. Mai 2021, Stuttgart, Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO, 351–362.Google Scholar
Cattaneo, M.E. (2011), “Belief functions combination without the assumption of independence of the information sources”, International Journal of Approximate Reasoning, Vol. 52 No. 3, pp. 299–315. https://doi.org/10.1016/j.ijar.2010.10.006.CrossRefGoogle Scholar
Chalupnik, M.J., Wynn, D.C. and Clarkson, P.J. (2009), “Approaches to mitigate the impact of uncertainty in development processes”, in Norell Bergendahl, M., Grimheden, M., Leifer, L., Skogstad, P. and Lindemann, U. (Eds.), Proceedings of the 17th International Conference on Engineering Design (ICED 09): Design Processes, 24.-27.08. 2009, Palo Alto, CA, Design Society, pp. 459–470.Google Scholar
Dajsuren, Y. and van den Brand, M. (2019), “Automotive Software Engineering. Past, Present, and Future”, in Dajsuren, Y. and van den Brand, M. (Eds.), Automotive Systems and Software Engineering: State of the Art and Future Trends, Springer International Publishing, Cham, pp. 3–8. https://doi.org/10.1007/978-3-030-12157-0_1.CrossRefGoogle Scholar
Dubois, D. (2006), “Possibility Theory and Statistical Reasoning”, Computational Statistics & Data Analysis, Vol. 51 No. 1, pp. 47–69. https://doi.org/10.1016/j.csda.2006.04.015.CrossRefGoogle Scholar
Ebert, C. and Man, J. de (2005), “Requirements uncertainty: influencing factors and concrete improvements”, in Proceedings of the 27th International Conference on Software Engineering (ICSE 05), May 15-21, 2005, St. Louis, MO, USA, Association for Computing Machinery, New York, NY, pp. 553–560. https://doi.org/10.1109/ICSE.2005.1553601.Google Scholar
Foith-Förster, P., Wiedenmann, M., Seichter, D. and Bauernhansl, T. (2016), “Axiomatic Approach to Flexible and Changeable Production System Design”, Procedia CIRP, Vol. 53, pp. 8–14. https://doi.org/10.1016/j.procir.2016.05.001.CrossRefGoogle Scholar
Gembarski, P.C., Plappert, S. and Lachmayer, R. (2021), “Making design decisions under uncertainties. Probabilistic reasoning and robust product design”, Journal of Intelligent Information Systems. https://doi.org/10.1007/s10844-021-00665-6.CrossRefGoogle Scholar
Scholar, Google (2023), “Keyword Search "Uncertain Requirements"”.Google Scholar
Han, X., Li, R., Wang, J., Ding, G. and Qin, S. (2020), “A systematic literature review of product platform design under uncertainty”, Journal of Engineering Design, Vol. 31 No. 5, pp. 266–296. https://doi.org/10.1080/09544828.2019.1699036.CrossRefGoogle Scholar
Herrmann, F., Block, L. and Riedel, O. (2023), “An Integrated Approach for Resilient Value Creation Among the Lifecycle. Using the Automotive Industry as an Example”, in Huang, C.-Y., Dekkers, R., Chiu, S.F., Popescu, D. and Quezada, L. (Eds.), Intelligent and Transformative Production in Pandemic Times: Proceedings of the 26th International Conference on Production Research, Springer International Publishing; Imprint Springer, Cham. https://doi.org/10.1007/978-3-031-18641-7_29.Google Scholar
Ishizuka, M. (1982), “An Extension of Dempster & Shafer's Theory to Fuzzy Set for Constructing Expert Systems”, Produktionsforschung, Vol. 34 No. 7, pp. 312–315.Google Scholar
Kang, N., Bayrak, A.E. and Papalambros, P.Y. (2018), “Robustness and Real Options for Vehicle Design and Investment Decisions Under Gas Price and Regulatory Uncertainties”, Journal of Mechanical Design, Vol. 140 No. 10. https://doi.org/10.1115/1.4040629.Google Scholar
Kreye, M., Goh, Y.M. and Newnes, L. (2011), “Manifestation of uncertainty. A classification”, in Culley, S.J., Hicks, B.J., McAloone, T.C., Howard, T.J. and Clarkson, J. (Eds.), Proceedings of the 18th International Conference on Engineering Design: ICED11, 15.08.2011-19.08.2011, Lyngby/Copenhagen, Design Society, pp. 96–107.Google Scholar
Letier, E., Stefan, D. and Barr, E.T. (2014), “Uncertainty, risk, and information value in software requirements and architecture”, in Jalote, P., Briand, L. and van der Hoek, A. (Eds.), Proceedings of the 36th International Conference on Software Engineering, Hyderabad India, ACM, New York, NY, USA, pp. 883–894. https://doi.org/10.1145/2568225.2568239.Google Scholar
Lucas, C. and Araabi, B.N. (1999), “Generalization of the Dempster-Shafer Theory. A Fuzzy-Valued Measure”, IEEE Transactions on Fuzzy Systems, Vol. 7 No. 3, pp. 255–270. https://doi.org/10.1109/91.771083.CrossRefGoogle Scholar
Luft, T. and Wartzack, S. (2014), “Klassifikation und Handhabung von Unsicherheiten zur entwicklungsbegleitenden Erfassung des Produktreifegrades”, in Stelzer, R. (Ed.), Entwerfen, Entwickeln, Erleben 2014: Beiträge zur virtuellen Produktentwicklung und Konstruktionstechnik, 26.06.2014-27.06.2014, Dresden, TUDpress, Dresden, pp. 535–549.Google Scholar
Mahler, R.P. (1995), “Combining ambiguous evidence with respect to ambiguous a priori knowledge. Part II: Fuzzy logic”, Fuzzy Sets and Systems, Vol. 75 No. 3, pp. 319–354. https://doi.org/10.1016/0165-0114(94)00386-L.CrossRefGoogle Scholar
McManus, H. and Hastings, D. (2005), “A Framework for Understanding Uncertainty and its Mitigation and Exploitation in Complex Systems”, INCOSE International Symposium, Vol. 15 No. 1, pp. 484–503. https://doi.org/10.1002/j.2334-5837.2005.tb00685.x.CrossRefGoogle Scholar
North, K. and Güldenberg, S. (2008), Produktive Wissensarbeit(er): Antworten auf die Management-Herausforderung des 21. Jahrhunderts, 1st ed., Gabler, Wiesbaden.CrossRefGoogle Scholar
Pohl, K. (2008), Requirements engineering: Grundlagen, Prinzipien, Techniken, 2nd ed., dpunkt, Heidelberg.Google Scholar
Schäuffele, J. and Zurawka, T. (2016), Automotive Software Engineering: Grundlagen, Prozesse, Methoden und Werkzeuge effizient einsetzen, ATZ/MTZ-Fachbuch, 6th ed., Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-11815-0.Google Scholar
Shenoy, P.P. (2020), “An expectation operator for belief functions in the Dempster–Shafer theory”, International Journal of General Systems, Vol. 49 No. 1, pp. 112–141. https://doi.org/10.1080/03081079.2019.1658756.CrossRefGoogle Scholar
Smets, P. and Kennes, R. (2008), “The Transferable Belief Model”, in Yager, R.R. and Liu, L. (Eds.), Classic Works of the Dempster-Shafer Theory of Belief Functions, Studies in Fuzziness and Soft Computing, Vol. 219, Springer, Berlin, Heidelberg, pp. 693–736. https://doi.org/10.1007/978-3-540-44792-4_28.CrossRefGoogle Scholar
Walker, W.E., Harremoës, P., Rotmans, J., van der Sluijs, J.P., van Asselt, M., Janssen, P. and von Krauss, Krayer, M.P. (2003), “Defining Uncertainty. A Conceptual Basis for Uncertainty Management in Model-Based Decision Support”, Integrated Assessment, Vol. 4 No. 1, pp. 5–17. https://doi.org/10.1076/iaij.4.1.5.16466.CrossRefGoogle Scholar
Whittle, J., Sawyer, P., Bencomo, N., Cheng, B.H.C. and Bruel, J.-M. (2010), “RELAX. A language to address uncertainty in self-adaptive systems requirement”, Requirements Engineering, Vol. 15 No. 2, pp. 177–196. https://doi.org/10.1007/s00766-010-0101-0.CrossRefGoogle Scholar
Yager, R.R. (1982), “Generalized Probabilities of Fuzzy Events from Fuzzy Belief Structures”, Information Sciences, Vol. 28 No. 1, pp. 45–62. https://doi.org/10.1016/0020-0255(82)90031-7.CrossRefGoogle Scholar
Yazdi, M. and Kabir, S. (2020), “Fuzzy evidence theory and Bayesian networks for process systems risk analysis”, Human and Ecological Risk Assessment, Vol. 26 No. 1, pp. 57–86. https://doi.org/10.1080/10807039.2018.1493679.CrossRefGoogle Scholar
Yen, J. (2008), “Generalizing the Dempster–Shafer Theory to Fuzzy Sets”, in Yager, R.R. and Liu, L. (Eds.), Classic Works of the Dempster-Shafer Theory of Belief Functions, Studies in Fuzziness and Soft Computing, Vol. 219, Springer, Berlin, Heidelberg, pp. 529–554. https://doi.org/10.1007/978-3-540-44792-4_21.CrossRefGoogle Scholar
Yu, E., Lapouchnian, A. and Deng, S. (2013), “Adapting to uncertain and evolving enterprise requirements. The case of business-driven business intelligence”, in Seventh International Conference on Research Challenges in Information Science, 29.05.2013 - 31.05.2013, Paris, France, IEEE, pp. 1–12. https://doi.org/10.1109/RCIS.2013.6577687.CrossRefGoogle Scholar