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  • 1.
    Abbas, Nadeem
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Towards autonomic software product lines2011In: SPLC '11 Proceedings of the 15th International Software Product Line Conference, Volume 2, ACM Press, 2011, p. 44:1-44:8Conference paper (Refereed)
    Abstract [en]

    We envision an Autonomic Software Product Line (ASPL). The ASPL is a dynamic software product line that supports self adaptable products. We plan to use reflective architecture to model and develop ASPL. To evaluate the approach, we have implemented three autonomic product lines which show promising results. The ASPL approach is at initial stages, and require additional work. We plan to exploit online learning to realize more dynamic software product lines to cope with the problem of product line evolution. We propose on-line knowledge sharing among products in a product line to achieve continuous improvement of quality in product line products.

  • 2.
    Abbas, Nadeem
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Andersson, Jesper
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Löwe, Welf
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Autonomic Software Product Lines (ASPL)2010In: ECSA '10 Proceedings of the Fourth European Conference on Software Architecture: Companion Volume / [ed] Carlos E. Cuesta, ACM Press, 2010, p. 324-331Conference paper (Refereed)
    Abstract [en]

    We describe ongoing work on a variability mechanism for Autonomic Software Product Lines (ASPL). The autonomic software product lines have self-management characteristics that make product line instances more resilient to context changes and some aspects of product line evolution. Instances sense the context, selects and bind the best component variants to variation-points at run-time. The variability mechanism we describe is composed of a profile guided dispatch based on off-line and on-line training processes. Together they form a simple, yet powerful variability mechanism that continuously learns, which variants to bind given the current context and system goals.