Selecting highly optimal architectural feature sets with Filtered Cartesian Flattening

Feature modeling is a common method used to capture the variability in a configurable application. A key challenge developers face when using a feature model is determining how to select a set of features for a variant that simultaneously satisfy a series of resource constraints. This paper presents an approximation technique for selecting highly optimal feature sets while adhering to resource limits. The paper provides the following contributions to configuring application variants from feature models: (1) we provide a polynomial time approximation algorithm for selecting a highly optimal set of features that adheres to a set of resource constraints, (2) we show how this algorithm can incorporate complex configuration constraints; and (3) we present empirical results showing that the approximation algorithm can be used to derive feature sets that are more than 90%+ optimal.