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Towards Autonomic Software Product Lines (ASPL) - A Technical Report
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics. (AdaptWise)
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics. (AdaptWise)ORCID iD: 0000-0001-5471-551X
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.ORCID iD: 0000-0002-7565-3714
2011 (English)Report (Other academic)
Abstract [en]

This report describes a work in progress to develop Autonomic Software Product Lines (ASPL). The ASPL is a dynamic software product line approach with a novel variability handling mechanism that enables traditional software product lines to adapt themselves at runtime in response to changes in their context, requirements and business goals. The ASPL variability mechanism is composed of three key activities: 1) context-profiling, 2) context-aware composition, and 3) online learning. Context-profiling is an offline activity that prepares a knowledge base for context-aware composition. The context-aware composition uses the knowledge base to derive a new product or adapts an existing product based on a product line's context attributes and goals. The online learning optimizes the knowledge base to remove errors and suboptimal information and to incorporate new knowledge. The three activities together form a simple yet powerful variability handling mechanism that learns and adapts a system at runtime in response to changes in system context and goals. We evaluated the ASPL variability mechanism on three small-scale software product lines and got promising results. The ASPL approach is, however, is yet at an initial stage and require improved development support with more rigorous evaluation. 

Place, publisher, year, edition, pages
2011. , p. 20
Keywords [en]
Software Variability, Self-adaptive Software Systems, Software Product Lines, Context-Aware Composition, Online Learning, Autonomic Software Product Lines
National Category
Software Engineering Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science; Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-71492OAI: oai:DiVA.org:lnu-71492DiVA, id: diva2:1189789
Available from: 2018-03-12 Created: 2018-03-12 Last updated: 2018-04-26Bibliographically approved

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Abbas, NadeemAndersson, JesperLöwe, Welf

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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