• 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