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ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Author
Knauss, A.; Damian, D.; Franch, X.; Rook, A.; Müller, H.; Thomo, A.
Type of activity
Journal article
Journal
Information and software technology
Date of publication
2016-02
Volume
70
First page
85
Last page
99
DOI
https://doi.org/10.1016/j.infsof.2015.10.001 Open in new window
Repository
http://hdl.handle.net/2117/102007 Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S0950584915001676 Open in new window
Abstract
Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems. Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), ...
Citation
Knauss, A., Damian, D., Franch, X., Rook, A., Müller, H., Thomo, A. ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime. "Information and software technology", Febrer 2016, vol. 70, p. 85-99.
Keywords
Contextual requirements, Machine learning, Operationalization, Requirements engineering, Self-adaptive systems
Group of research
GESSI - Group of Software and Service Engineering
inSSIDE - integrated Software, Service, Information and Data Engineering

Participants

  • Knauss, Alessia  (author)
  • Damian, Daniela  (author)
  • Franch Gutierrez, Javier  (author)
  • Rook, Angela  (author)
  • Müller, Haussi A.  (author)
  • Thomo, Alex  (author)

Attachments