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The Uncertainty Interaction Problem in Self-Adaptive Systems

Camara, Javier; Troya1, Javier; Vallecillo, Antonio; Bencomo, Nelly; Calinescu, Radu; Cheng, Betty; Garlan, David; Schmerl, Bradley


Javier Camara

Javier Troya1

Antonio Vallecillo

Radu Calinescu

Betty Cheng

David Garlan

Bradley Schmerl


The problem of mitigating uncertainty in self-adaptation has driven much of the research proposed in the area of software engineering for self-adaptive systems in the last decade. Although many solutions have already been proposed, most of them tend to tackle specific types, sources, and dimensions of uncertainty (e.g., in goals, resources, adaptation functions) in isolation. A special concern are the aspects associated with uncertainty modeling in an integrated fashion. Different uncertainties are rarely independent and often compound, affecting the satisfaction of goals and other system properties in subtle and often unpredictable ways. Hence, there is still limited understanding about the specific ways in which uncertainties from various sources interact and ultimately affect the properties of self-adaptive, software-intensive systems. In this SoSym expert voice, we introduce the Uncertainty Interaction Problem as a way to better qualify the scope of the challenges with respect to representing different types of uncertainty while capturing their interaction in models employed to reason about self-adaptation. We contribute a characterization of the problem and discuss its relevance in the context of case studies taken from two representative application domains. We posit that the Uncertainty Interaction Problem should drive future research in software engineering for autonomous and self-adaptive systems, and therefore, contribute to evolving uncertainty modeling towards holistic approaches that would enable the construction of more resilient self-adaptive systems.


Camara, J., Troya1, J., Vallecillo, A., Bencomo, N., Calinescu, R., Cheng, B., …Schmerl, B. (2022). The Uncertainty Interaction Problem in Self-Adaptive Systems. Software and Systems Modeling, 21(4), 1277-1294.

Journal Article Type Article
Acceptance Date Jul 19, 2022
Online Publication Date Aug 17, 2022
Publication Date 2022-08
Deposit Date May 27, 2022
Publicly Available Date Aug 18, 2023
Journal Software and Systems Modeling
Print ISSN 1619-1366
Electronic ISSN 1619-1374
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 21
Issue 4
Pages 1277-1294


Accepted Journal Article (991 Kb)

Copyright Statement
The version of record of this article, first published in Software and Systems Modeling, is available online at Publisher’s website:

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