Dynamic Reconfiguration of Feature Models: an Algorithm and its Evaluation

Sabine Moisan 1 Jean-Paul Rigault 1
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : This paper deals with dynamic adaption of software architecture in response to context changes. In the line of “models at run time”, we keep a model of the system and its context in parallel with the running system itself. We adopted an enriched Feature Model approach to express the variability of the architecture as well as of the context. A context change is transformed into a set of feature modifications (selection/deselection) that we validate against the feature model to yield a new suitable and valid architecture configuration. Then we update the model view of the configuration and the running system architecture accordingly. The paper focuses on the feature model reconfiguration step and details the algorithms and heuristics that implement our adaptation rules. The approach is illustrated with a simple example borrowed from the videosurveillance domain. The efficiency of the algorithm is evaluated on randomly generated feature models (from 60 to 1400 features). Our results show that in our target applications (video analysis), the processing time of a context change may be considered negligible.
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[Research Report] RR-8972, INRIA Sophia Antipolis. 2016, pp.16
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Sabine Moisan, Jean-Paul Rigault. Dynamic Reconfiguration of Feature Models: an Algorithm and its Evaluation. [Research Report] RR-8972, INRIA Sophia Antipolis. 2016, pp.16. 〈hal-01392796〉

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