Abstrakt
Feature models are a widely used modeling notation for variability and commonality management in software product line (SPL) engineering. In order to keep an SPL and its feature model aligned, feature models must be changed by including/excluding new features and products, either because faults in the model are found or to reflect the normal evolution of the SPL.
The modification of the feature model able to satisfy these change requirements can be complex and error-prone. In this paper, we present a method that is able to automatically update a feature model in order to satisfy a given update request.
Our method is based on an evolutionary algorithm and it iteratively applies structure-preserving mutations to the original model, until the model is completely updated. We evaluate the process on real-world feature models.
Although our approach does not guarantee to completely update all possible feature models, empirical analysis shows that, on average, more than 80% of requested changes are applied.