ARRoW: Automatic Runtime Reappraisal of Weights for Self-Adaptation

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(SAS) requires the runtime trade-off of multiple non-functional requirements
(NFRs) and the costs-benefits analysis of the alternative
solutions. Usually, it is required the specification of the weights
(a.k.a. preferences) associated with the NFRs and decision-making
strategies. These preferences are traditionally defined at designtime.
[Questions/Problems] A big challenge is the need to deal with
unsuitable preferences, based on empirical evidence available at runtime,
and which may not agree anymore with previous assumptions.
Therefore, new techniques are needed to systematically reassess
the current preferences according to empirical evidence collected at
runtime. [Principal ideas/ results] We present ARRoW (Automatic
Runtime Reappraisal ofWeights) to support the dynamic update of
preferences/weights associated with the NFRs and decision-making
strategies in SAS, while taking into account the current levels of
satisficement that NFRs can reach during the system’s operation.
[Contribution] To developed ARRoW, we have extended the Primitive
Cognitive Network Process (P-CNP), a version of the Analytical
Hierarchy Process (AHP), to enable the handling and update
of weights during runtime. Specifically, in this paper, we show a
formalization for the specification of the decision-making of a SAS
in terms of NFRs, the design decisions and their corresponding
weights as a P-CNP problem. We also report on how the P-CNP
has been extended to be used at runtime. We show how the propagation
of elements of P-CNP matrices is performed in such a way
that the weights are updated to therefore, improve the levels of
satisficement of the NFR to better match the current environment
during runtime. ARRoW leverages the Bayesian learning process
underneath, which on the other hand, provides the mechanism
to get access to evidence about the levels of satisficement of the
NFR. The experiments have been applied to a case study of the
networking application domain where the decision-making has
been improved.

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Publication date8 Apr 2019
Publication titleThe 12th Edition of the Requirements Engineering Track (RE-Track'19) is part of the 34rd ACM Symposium on Applied Computing. SAC 2019
ISBN (Print)978-1-4503-5933-7/19/04
Original languageEnglish
Event34th ACM/SIGAPP Symposium On Applied Computing - Limassol, Cyprus
Duration: 8 Apr 201912 Apr 2019


Conference34th ACM/SIGAPP Symposium On Applied Computing

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