DocumentCode :
3685972
Title :
QuantUn: Quantification of uncertainty for the reassessment of requirements
Author :
Nelly Bencomo
Author_Institution :
ALICE: The Aston Lab for Intelligent Collectives Engineering, School of Engineering and Applied Science, Aston University, UK
fYear :
2015
Firstpage :
236
Lastpage :
240
Abstract :
Self-adaptive systems (SASs) should be able to adapt to new environmental contexts dynamically. The uncertainty that demands this runtime self-adaptive capability makes it hard to formulate, validate and manage their requirements. QuantUn is part of our longer-term vision of requirements reflection, that is, the ability of a system to dynamically observe and reason about its own requirements. QuantUn´s contribution to the achievement of this vision is the development of novel techniques to explicitly quantify uncertainty to support dynamic re-assessment of requirements and therefore improve decision-making for self-adaption. This short paper discusses the research gap we want to fill, present partial results and also the plan we propose to fill the gap.
Keywords :
"Uncertainty","Bayes methods","Runtime","Decision making","Cognition","Adaptive systems","Adaptation models"
Publisher :
ieee
Conference_Titel :
Requirements Engineering Conference (RE), 2015 IEEE 23rd International
Type :
conf
DOI :
10.1109/RE.2015.7320429
Filename :
7320429
Link To Document :
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