Extending feature models with relative cardinalities

Abstract

Feature modeling is widely used to capture and manage commonalities and variabilities in software product lines. Cardinality-based feature models are used when variability applies not only to the selection or exclusion of features but also to the number of times a feature can be included in a product. Feature cardinalities are usually considered to apply in either a local or global scope. However, we have identified that these interpretations are insufficient to capture the variability of cloud environments. In this paper, we redefine cardinality-based feature models to allow multiple relative cardinalities between features and we discuss the effects of relative cardinalities on feature modeling semantics, consistency and cross-tree constraints. To evaluate our approach we conducted an analysis of relative cardinalities in four cloud computing providers. In addition, we developed tools for reasoning on feature models with relative cardinalities and performed experiments to verify the performance and scalability of the approach. The results from our study indicate that extending feature models with relative cardinalities is feasible and improves variability modeling, particularly in the case of cloud environments.