A survey on preferences of quality attributes in the decision-making for self-adaptive systems:The bad, the good and the ugly

Abstract

Different techniques have been used to specify preferences for quality attributes and decision-making strategies of self-adaptive systems (SAS). These preferences are defined during requirement specification and design time. Further, it is well known that correctly identifying the preferences associated with the quality attributes is a major difficulty. This is exacerbated in the case of SAS, as the preferences defined at design time may not apply to contexts found at runtime. This paper aims at making an exploration of the research landscape that have addressed decision-making and quality attribute preferences specification for selfadaptation, in order to identify new techniques that can improve the current state-of-the-art of decision-making to support self-adaptation. In this paper we (1) review different techniques that support decisionmaking for self-adaptation and identify limitations with respect to the identification of preferences and weights (i.e. the research gap), (2) identify existing solutions that deal with current limitations.

Divisions: College of Engineering & Physical Sciences
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College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Event Title: 20th Workshop em Engenharia de Requisitos, WER 2017 - 20th Requirements Engineering Workshop, WER 2017
Event Type: Other
Event Dates: 2017-05-22 - 2017-05-23
Uncontrolled Keywords: Decision making,Preference trade-off,Quality attributes,Self-adaptation,Human-Computer Interaction,Software,Signal Processing,Artificial Intelligence
ISBN: 9789873806988
Last Modified: 08 Jan 2024 09:53
Date Deposited: 17 Aug 2017 13:25
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2017
Published Online Date: 2017-05-23
Accepted Date: 2017-05-01
Authors: Paucar, Luis H.Garcia (ORCID Profile 0000-0003-2915-0830)
Bencomo, Nelly (ORCID Profile 0000-0001-6895-1636)

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