Skip to main content

Living with Uncertainty in Model-Based Development

  • Chapter
  • First Online:

Abstract

Uncertainty is present in model-based developments in many different ways. In the context of composing model-based analysis tools, this chapter discusses how the combination of different models can increase or decrease the overall uncertainty. It explores how such uncertainty could be more explicitly addressed and systematically managed, with the goal of defining a conceptual framework to deal with and manage it. We proceed towards this goal both with a theoretical reasoning and a practical application through an example of designing a peer-to-peer file-sharing protocol. We distinguish two main steps: (i) software system modelling and (ii) model-based performance analysis by highlighting the challenges related to the awareness that model-based development in software engineering needs to coexist with uncertainty. This core chapter addresses Challenge 5 introduced in Chap. 3 of this book (living with uncertainty).

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   159.00
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Davide Arcelli, Vittorio Cortellessa, and Catia Trubiani. “Performance-Based Software Model Refactoring in Fuzzy Contexts”. In: International Conference on Fundamental Approaches to Software Engineering, FASE. 2015, pp. 149–164.

    Google Scholar 

  2. Manuel F. Bertoa, Nathalie Moreno, Gala Barquero, Loli Burgueño, Javier Troya, and Antonio Vallecillo. “Expressing Measurement Uncertainty in OCL/UML Datatypes”. In: Modelling Foundations and Applications. 2018, pp. 46–62.

    Google Scholar 

  3. Simona Bernardi, José Merseguer, and Dorina C. Petriu. Model-Driven Dependability Assessment of Software Systems. Springer, 2013.

    Book  Google Scholar 

  4. Javier Cámara, Wenxin Peng, David Garlan, and Bradley R. Schmerl. “Reasoning about sensing uncertainty and its reduction in decision-making for self-adaptation”. In: Science of Computer Programming 167 (2018), pp. 51–69.

    Google Scholar 

  5. Valeria Cardellini, Tihana Galinac Grbac, Matteo Nardelli, Nikola Tankovic, and Hong-Linh Truong. “Qos-based elasticity for service chains in distributed edge cloud environments”. In: Autonomous Control for a Reliable Internet of Services. 2018, pp. 182–211.

    Google Scholar 

  6. Francesca Campolongo, Jessica Cariboni, and Andrea Saltelli. “An effective screening design for sensitivity analysis of large models”. In: Environmental Modelling & Software 22.10 (2007), pp. 1509–1518. ISSN: 1364-8152. https://doi.org/10.1016/j.envsoft.2006.10.004.

  7. Vittorio Cortellessa, Antinisca DiMarco, and Paola Inverardi. Model-Based Software Performance Analysis. Springer, 2011. https://doi.org/10.1007/978-3-642-13621-4.

    Book  Google Scholar 

  8. Vittorio Cortellessa and Raffaela Mirandola. “Deriving a Queueing Network based Performance Model from UML Diagrams”. In: Second International Workshop on Software and Performance, WOSP. 2000, pp. 58–70.

    Google Scholar 

  9. Americo Cunha, Rafael Nasser, Rubens Sampaio, Hélio Lopes, and Karin Breitman. “Uncertainty quantification through the Monte Carlo method in a cloud computing setting”. In: Computer Physics Communications 185.5 (2014), pp. 1355–1363. https://doi.org/10.1016/j.cpc.2014.01.006.

  10. Naeem Esfahani and Sam Malek. “Uncertainty in Self-Adaptive Software Systems”. In: Software Engineering for Self-Adaptive Systems II. 2013, pp. 214–238.

    Google Scholar 

  11. Leire Etxeberria, Catia Trubiani, Vittorio Cortellessa, and Goiuria Sagardui. “Performance-based selection of software and hardware features under parameter uncertainty”. In: International Conference on Quality of Software Architectures, QoSA. 2014, pp. 23–32.

    Google Scholar 

  12. Michalis Famelis and Marsha Chechik. “Managing design-time uncertainty”. In: Software & Systems Modeling 18.2 (2019), pp. 1249–1284.

    Google Scholar 

  13. Michalis Famelis, Rick Salay, and Marsha Chechik. “Partial models: Towards modeling and reasoning with uncertainty”. In: 34th International Conference on Software Engineering, ICSE. 2012, pp. 573–583.

    Google Scholar 

  14. Robert Heinrich, Francisco Durán, Carolyn L. Talcott, and Steffen Zschaler (eds.) Composing Model-Based Analysis Tools. Springer, 2021. https://doi.org/10.1007/978-3-030-81915-6.

  15. Pooyan Jamshidi, Amir Sharifloo, Claus Pahl, Hamid Arabnejad, Andreas Metzger, and Giovani Estrada. “Fuzzy self-learning controllers for elasticity management in dynamic cloud architectures”. In: International Conference on Quality of Software Architectures, QoSA. 2016, pp. 70–79.

    Google Scholar 

  16. Anne-Laure Jousselme, Patrick Maupin, and éloi Bossé. “Uncertainty in a situation analysis perspective”. In: 6th International Conference of Information Fusion. 2003, pp. 1207–1214. https://doi.org/10.1109/ICIF.2003.177375.

  17. Leonard Kleinrock. Queueing Systems Vol. 1:Theory. Wiley, 1975.

    Google Scholar 

  18. Indika Meedeniya, Irene Moser, Aldeida Aleti, and Lars Grunske. “Architecture-based reliability evaluation under uncertainty”. In: International Conference on Component- Based Software Engineering and Software Architecture, CompArch. 2011, pp. 85–94.

    Google Scholar 

  19. James Martin and James J. Odell. Object-Oriented Methods: a Foundation. 2nd Edition. Prentice Hall, 1997.

    Google Scholar 

  20. OMG. UML Profile for MARTE. Version 1.2, formal/19-04-01, April 2019. Object Management Group.

    Google Scholar 

  21. OMG. Unified Modeling Language. Version 2.5.1, formal/17-12-05, December 2017. Object Management Group.

    Google Scholar 

  22. Diego Perez-Palacin, José Merseguer, José I. Requeno, M. Guerriero, Elisabetta Di Nitto, and D. A. Tamburri. “A UML Profile for the Design, Quality Assessment and Deployment of Data-intensive Applications”. In: Software & Systems Modeling 18.6 (2019), pp. 3577–3614. https://doi.org/10.1007/s10270-019-00730-3.

  23. Mattia Padulo and Marin D. Guenov. “A methodological perspective on Computational Engineering Design under uncertainty”. In: European Congress on Computational Methods in Applied Sciences and Engineering. 2012, pp. 7509–7528. https://eccomas2012.conf.tuwien.ac.at/.

  24. Diego Perez-Palacin and Raffaela Mirandola. “Dealing with Uncertainties in the Performance Modelling of Software Systems”. In: 10th International ACM Sigsoft Conference on Quality of Software Architectures, QoSA. 2014, pp. 33–42. https://doi.org/10.1145/2602576.2602582.

  25. Diego Perez-Palacin and Raffaela Mirandola. “Uncertainties in the Modeling of Self- Adaptive Systems: A Taxonomy and an Example of Availability Evaluation”. In: 5th ACM/SPEC International Conference on Performance Engineering, ICPE. 2014, pp. 3–14. https://doi.org/10.1145/2568088.2568095.

  26. Alaleh Razmjoo, Petros Xanthopoulos, and Qipeng Phil Zheng. “Online Feature Importance Ranking Based on Sensitivity Analysis”. In: Expert Systems with Applications 85.C (2017), pp. 397–406. https://doi.org/10.1016/j.eswa.2017.05.016.

  27. Tiago Prince Sales, Fernanda Baião, Giancarlo Guizzardi, João Paulo A Almeida, Nicola Guarino, and John Mylopoulos. “The common ontology of value and risk”. In: International Conference on Conceptual Modeling. 2018, pp. 121–135.

    Google Scholar 

  28. Ilya M. Sobol. “Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates”. In: Mathematics and Computers in Simulation 55.1 (2001), pp. 271–280. https://doi.org/10.1016/S0378-4754(00)00270-6.

  29. Catia Trubiani and Raffaela Mirandola. “Continuous Rearchitecting of QoS Models: Collaborative Analysis for Uncertainty Reduction”. In: European Conference on Software Architecture (ECSA). 2017, pp. 40–48.

    Google Scholar 

  30. Catia Trubiani, Indika Meedeniya, Vittorio Cortellessa, Aldeida Aleti, and Lars Grunske. “Model-based performance analysis of software architectures under uncertainty”. In: International Conference on Quality of Software Architectures, QoSA. 2013, pp. 69–78.

    Google Scholar 

  31. Man Zhang, Shaukat Ali, Tao Yue, Roland Norgren, and Oscar Okariz. “Uncertainty- Wise Cyber-Physical System test modeling”. In: Software & Systems Modeling 18.2 (2019), pp. 1379–1418. https://doi.org/10.1007/s10270-017-0609-6.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Diego Perez Palacin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bernardi, S. et al. (2021). Living with Uncertainty in Model-Based Development. In: Heinrich, R., Durán, F., Talcott, C., Zschaler, S. (eds) Composing Model-Based Analysis Tools. Springer, Cham. https://doi.org/10.1007/978-3-030-81915-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-81915-6_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-81914-9

  • Online ISBN: 978-3-030-81915-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics