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