Information Reuse and Stochastic Search

Kinneer, Cody; Garlan, David; Goues, Claire Le

doi:10.1145/3440119

Cited by 9 publications

(1 citation statement)

References 48 publications

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“…Exploring uncertainty with "What if?" scenarios (e.g., using game theory) enables the developer to explore uncertainty with respect to specific operational contexts [6,26].…”

Section: Exploration Challengesmentioning

confidence: 99%

The uncertainty interaction problem in self-adaptive systems

et al. 2022

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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.

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“…Exploring uncertainty with "What if?" scenarios (e.g., using game theory) enables the developer to explore uncertainty with respect to specific operational contexts [6,26].…”

Section: Exploration Challengesmentioning

confidence: 99%

The uncertainty interaction problem in self-adaptive systems

et al. 2022

Self Cite

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Towards Reactive Planning with Digital Twins and Model-Driven Optimization

Eisenberg

Lehner

Šindelář

et al. 2022

Lecture Notes in Computer Science

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Digital Twins are emerging in several domains. They allow to connect various models with running systems based on bi-directional data exchange. Thus, design models can be extended with runtime views which also opens the door for many additional techniques such as identifying unexpected system changes during runtime. However, dedicated reactions to these unexpected changes, such as adapting an existing plan which has been computed in advance and may no longer be seen beneficial, are still often neglected in Digital Twins. To tackle this shortcoming, we propose so-called reactive planning that integrates Digital Twins with planning approaches to react to unforeseen changes during plan execution. In particular, we introduce an extended Digital Twin architecture which allows to integrate existing model-driven optimization frameworks. Based on this integration, we present different strategies how the replanning can be performed by utilizing the information and services available in Digital Twins. We evaluate our approach for a stack allocation case study. This evaluation yields promising results on how to effectively improve existing plans during runtime, but also allows to identify future lines of research in this area.

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