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Computer Science > Software Engineering

arXiv:2211.17218 (cs)
[Submitted on 30 Nov 2022]

Title:Specification Architectural Viewpoint for Benefit-Cost-Risk-Aware Decision-Making in Self-Adaptive Systems

Authors:Danny Weyns, Paris Avegriou, Radu Calinescu, Sara M. Hezavehi, Raffaela Mirandola, Diego Perez-Palacin
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Abstract:Over the past two decades, researchers and engineers have extensively studied the problem of how to enable a software system to deal with uncertain operating conditions. One prominent solution to this problem is self-adaptation, which equips a software system with a feedback loop that resolves uncertainties during operation and adapts the system to deal with them when necessary. Most self-adaptation approaches developed so far use decision-making mechanisms that focus on achieving a set of goals, i.e., that select for execution the adaptation option with the best estimated benefit. A few approaches have also considered the estimated (one-off) cost of executing the candidate adaptation options. We argue that besides benefit and cost, decision-making in self-adaptive systems should also consider the estimated risk the system or its users would be exposed to if an adaptation option were selected for execution. Balancing all three factors when evaluating the options for adaptation when mitigating uncertainty is essential, not only for satisfying the concerns of the stakeholders, but also to ensure safety and public acceptance of self-adaptive systems. In this paper, we present an ISO/IEC/IEEE 42010 compatible architectural viewpoint that considers the estimated benefit, cost, and risk as core factors of each adaptation option considered in self-adaptation. The viewpoint aims to support software architects responsible for designing robust decision-making mechanisms for self-adaptive systems.
Comments: 24 pages
Subjects: Software Engineering (cs.SE)
Cite as: arXiv:2211.17218 [cs.SE]
  (or arXiv:2211.17218v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2211.17218
arXiv-issued DOI via DataCite

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From: Danny Weyns [view email]
[v1] Wed, 30 Nov 2022 18:12:44 UTC (2,313 KB)
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