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https://dx.doi.org/10.48550/ar...
Article . 2017
License: arXiv Non-Exclusive Distribution
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Requirements-Driven Dynamic Adaptation to Mitigate Runtime Uncertainties for Self-Adaptive Systems

Authors: Yang, Zhuoqun; Zhang, Wei; Zhao, Haiyan; Jin, Zhi;

Requirements-Driven Dynamic Adaptation to Mitigate Runtime Uncertainties for Self-Adaptive Systems

Abstract

Self-adaptive systems are capable of adjusting their behavior to cope with the changes in environment and itself. These changes may cause runtime uncertainty, which refers to the system state of failing to achieve appropriate reconfigurations. However, it is often infeasible to exhaustively anticipate all the changes. Thus, providing dynamic adaptation mechanisms for mitigating runtime uncertainty becomes a big challenge. This paper suggests solving this challenge at requirements phase by presenting REDAPT, short for REquirement-Driven adAPTation. We propose an adaptive goal model (AGM) by introducing adaptive elements, specify dynamic properties of AGM by providing logic based grammar, derive adaptation mechanisms with AGM specifications and achieve adaptation by monitoring variables, diagnosing requirements violations, determining reconfigurations and execution. Our approach is demonstrated with an example from the Intelligent Transportation System domain and evaluated through a series of simulation experiments.

Self-adaptive systems, requirements modeling, runtime uncertainty, specification, dynamic adaptation

Keywords

Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, D.3.1, 68N30, FOS: Electrical engineering, electronic engineering, information engineering, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green