Dynamic Constraint Satisfaction Algorithm for Online Feature Model Reconfiguration

Abstract

Dynamically reconfigurable systems are able to respond to changes in their operational environments by reconfiguring themselves automatically. Dynamic software product lines are dynamically reconfigurable systems with an explicit variability model that guides the reconfiguration. In this work, feature models are used as the variability model. An emerging situation in the environment can lead to some relevant changes to the current configuration: some features must be activated, and some must be deactivated. Due to constraint propagation, the status of other features might need to be changed as well. However, considering the feature state migration cost, one would like to mitigate the cost to the greatest extent possible. Furthermore, the configuration with a proper cost has to be reached in an acceptable time. In this paper, we devised a set of feature model heuristics for a constraint satisfaction problem algorithm that considers the efficiency and the cost of feature state changes to be applied to the current configuration while confronting the changes in the environment so that the requirements of the new situation will be efficiently satisfied with the minimum cost.