Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-19T11:46:08.233Z Has data issue: false hasContentIssue false

HANDLING OF EXPLICIT UNCERTAINTY IN REQUIREMENTS CHANGE MANAGEMENT

Published online by Cambridge University Press:  27 July 2021

Iris Gräßler
Affiliation:
Paderborn University, Heinz Nixdorf Institute, Chair of Product Creation
Jens Pottebaum*
Affiliation:
Paderborn University, Heinz Nixdorf Institute, Chair of Product Creation
Christian Oleff
Affiliation:
Paderborn University, Heinz Nixdorf Institute, Chair of Product Creation
Daniel Preuß
Affiliation:
Paderborn University, Heinz Nixdorf Institute, Chair of Product Creation
*
Pottebaum, Jens, Paderborn University, Heinz Nixdorf Institute, Chair of Product Creation, Germany, [email protected]

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.

Innovation projects are characterized by numerous uncertainties. Typical concepts in development management like the application of safety coefficients imply limitations of the solution space. In contrast, explicit handling of uncertainties can support engineers in understanding the problem space and in utilising the full potential of the design space along iterative product development steps. As a result from literature analysis, there is a lack of a support for product development that addresses the specific problem of uncertainty and risk in the context of requirement changes. The aim of the contribution at hand is to enhance the efficient development of complex interdisciplinary systems by enabling uncertainty handling in requirements change management. Based on a classification of uncertainty types resulting in a descriptive model, risk management measures are identified to support requirements engineers. The proposed method includes identification & modelling, analysis, treatment and monitoring of risks and counter-measures. By applying this method, engineers are supported in adopting agile approaches and enabling flexible Requirements Engineering.

Type
Article
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), 2021. Published by Cambridge University Press

References

Bandyszak, T., Daun, M., Tenbergen, B., Kuhs, P., Wolf, S. and Weyer, T. (2020), “Orthogonal Uncertainty Modeling in the Engineering of Cyber-Physical Systems”, IEEE Transactions on Automation Science and Engineering, pp. 116.10.1109/TASE.2020.2980726CrossRefGoogle Scholar
Casakin, H. and Badke-Schaub, P. (2017), “Sharedness of team mental models in the course of design-related interaction between architects and clients”, Design Science, Vol. 3.10.1017/dsj.2017.15CrossRefGoogle Scholar
Clarkson, P.J., Simons, C. and Eckert, C. (2004), “Predicting Change Propagation in Complex Design”, Journal of Mechanical Design, Vol. 126 No. 5, pp. 788797.10.1115/1.1765117CrossRefGoogle Scholar
Diederichs, M. (2012), Risikomanagement und Risikocontrolling, Verlag Franz Vahlen.Google Scholar
Fricke, E., Gebhard, B., Negele, H. and Igenbergs, E. (2000), “Coping with changes Causes, findings, and strategies”, Systems Engineering, Vol. 3 No. 4, pp. 169179.10.1002/1520-6858(2000)3:4<169::AID-SYS1>3.0.CO;2-W3.0.CO;2-W>CrossRefGoogle Scholar
Ehrlenspiel, K. (2007), Integrierte Produktentwicklung: Denkabläufe, Methodeneinsatz, Zusammenarbeit, Hanser, München.Google Scholar
Eifler, T. (2015), “Modellgestützte Methodik zur systematischen Analyse von Unsicherheit im Lebenslauf technischer Systeme”, Dissertation, Fachgebiet Produktentwicklung und Maschinenelemente (pmd), Technische Universität Darmstadt, Düsseldorf, 2015.Google Scholar
Gericke, K. (2011), “Enhancing Project Robustness: A Risk Management Perspective”, Dissertation, 2011.Google Scholar
Gleißner, W. and Wolfrum, M. (2019), “Grundlagen des Risikomanagements”, in Gleißner, W. and Wolfrum, M. (Eds.), Risikoaggregation und Monte-Carlo-Simulation: Schlüsseltechnologie für Risikomanagement und Controlling, essentials, Springer Fachmedien Wiesbaden, Wiesbaden, pp. 313.10.1007/978-3-658-24274-9_2CrossRefGoogle Scholar
Graessler, I., Oleff, C. and Scholle, P. (2020), “Method for Systematic Assessment of Requirement Change Risk in Industrial Practice”, Applied Sciences, Vol. 10 No. 23, p. 8697.10.3390/app10238697CrossRefGoogle Scholar
Gräßler, I. and Oleff, C. (2019), “Risikoorientierte Analyse und Handhabung von Anforderungsänderungen”, 30th Symposium Design for X (DFX 2019), 18-19 September 2019, Jesteburg, Germany, pp. 4960.10.35199/dfx2019.5CrossRefGoogle Scholar
Gräßler, I., Oleff, C. and Scholle, P. (2019), “Priorisierung von Anforderungen für die Entwicklung mechatronischer Systeme”, Fachtagung Mechatronik 2019.10.31224/osf.io/d82huCrossRefGoogle Scholar
Gräßler, I., Preuß, D. and Oleff, C. (2020), “Automatisierte Identifikation und Charakterisierung von Anforderungsabhängigkeiten – Literaturstudie zum Vergleich von Lösungsansätzen”, in Krause, D., Paetzold, K. and Wartzack, S. (Eds.), 31st Symposium Design for X (DFX2020), pp. 199208.10.35199/dfx2020.21CrossRefGoogle Scholar
Hein, P.H., Voris, N. and Morkos, B. (2018), “Predicting requirement change propagation through investigation of physical and functional domains”, Research in Engineering Design, Vol. 29 No. 2, pp. 309328.10.1007/s00163-017-0271-6CrossRefGoogle Scholar
Hoffmann, A., Hoffmann, H. and Janzen, A. (2013), “Anforderungsmuster im Requirements Engineering”, in Working Paper Series Nr. 2, Kassel.Google Scholar
IEEE (1990), “IEEE Standard Glossary of Software Engineering Terminology”, IEEE Std 610. 121990.Google Scholar
Jayatilleke, S. and Lai, R. (2018), “A systematic review of requirements change management”, Information and Software Technology, Vol. 93, pp. 163185.10.1016/j.infsof.2017.09.004CrossRefGoogle Scholar
Fernandes, J., Henriques, E., Silva, A. and Moss, M. A. (2018), “Requirements change in complex technical systems: an empirical study of root causes”, Research in Engineering Design, Vol. 26 No. 1, pp. 3755.10.1007/s00163-014-0183-7CrossRefGoogle Scholar
Kreye, M.E., Goh, Y.M. and Newnes, L.B. (2011), “Manifestation of Uncertainty – A classification”, in Culley, S.J., Hicks, B.J., McAloone, T.C., Howard, T.J. and Dong, A. (Eds.), Impacting society through engineering design: Proceedings of the 18th International Conference on Engineering Design (ICED 2011), Kopenhagen, Design Society, Glasgow, pp. 96107.Google Scholar
Mousavi, S. and Gigerenzer, G. (2014), “Risk, uncertainty, and heuristics”, Journal of Business Research, Vol. 67 No. 8, pp. 16711678.10.1016/j.jbusres.2014.02.013CrossRefGoogle Scholar
Neumann, M. (2017), “Ein modellbasierter Ansatz zur risikoorientierten Entwicklung innovativer Produkte”, Dissertation, Shaker Verlag GmbH, 2017.Google Scholar
Nonaka, I. and Takeuchi, H. (1995), The knowledge creating company: How Japanese companies create the dynamics of innovation, Oxford Univ. Press, New York.Google Scholar
Abrahamsson, P., Fronza, I., Moser, R., Vlasenko, J. and Pedrycz, W. (2011), “Predicting Development Effort from User Stories”, 2011 International Symposium on Empirical Software Engineering and Measurement.10.1109/ESEM.2011.58CrossRefGoogle Scholar
Pohl, K. (2010), Requirements engineering: Fundamentals, principles, and techniques, Springer, Berlin.10.1007/978-3-642-12578-2CrossRefGoogle Scholar
Pottebaum, J. and Gräßler, I. (2020), “Informationsqualität in der Produktentwicklung: Semantische Beschrei-bung inhärenter Unsicherheit in Produktmodellen”, Konstruktion, 72 (11-12), VDI-Verlag, pp. 76 - 83.10.37544/0720-5953-2020-11-12-76CrossRefGoogle Scholar
Song, Y.-W., Herzog, M. and Bender, B. (2019), “Understanding the Initial Requirements Definition in Early Design Phases”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1 No. 1, pp. 37513760.Google Scholar
Stachowiak, H. (1973), Allgemeine Modelltheorie, Springer, Wien.10.1007/978-3-7091-8327-4CrossRefGoogle Scholar
Thunnissen, D.P. (2003), “Uncertainty Classification for the Design and Development of Complex Systems”, paper presented at Annual Predictive Methods Conference, Newport Beach, California.Google Scholar
Troya, J., Moreno, N., Bertoa, M.F. and Vallecillo, A. (2021), “Uncertainty representation in software models: a survey”, Software and Systems Modeling, Vol. 82 No. 4, p. 707.Google Scholar
Vanini, U. (2012), Risikomanagement: Grundlagen; Instrumente; Unternehmenspraxis, Schäffer-PoeschelGoogle Scholar
Walden, D.D., Roedler, G.J., Forsberg, K., Hamelin, R.D. and Shortell, T.M. (Eds.) (2015), Systems engineering handbook: A guide for system life cycle processes and activities, 4. edition, Wiley, Hoboken, NJ.Google Scholar
Wang, R.Y. and Strong, D.M. (1996), “Beyond Accuracy: What Data Quality Means to Data Consumers”, Journal of Management Information Systems, Vol. 12 No. 4, pp. 533.10.1080/07421222.1996.11518099CrossRefGoogle Scholar