The Uncertainty Interaction Problem in Self-Adaptive Systems

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

Abstract

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.

Citation

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. https://doi.org/10.1007/s10270-022-01037-6

• 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
• DOI: https://doi.org/10.1007/s10270-022-01037-6
• Public URL: https://durham-repository.worktribe.com/output/1204037