Selecting highly optimal architectural feature sets with Filtered Cartesian Flattening

Software Product-lines (SPLs) are software architectures that use modular software components that can be reconfigured into different variants for different requirements sets. Feature modeling is a common method used to capture the configuration rules for an SPL architecture. A key challenge developers face when maintaining an SPL is determining how to select a set of architectural features for an SPL variant that simultaneously satisfy a series of resource constraints. This paper presents an approximation technique for selecting highly optimal architectural feature sets while adhering to resource limits. The paper provides the following contributions to configuring SPL architecture variants: (1) we provide a polynomial time approximation algorithm for selecting a highly optimal set of architectural features that adheres to a set of resource constraints, (2) we show how this algorithm can incorporate complex architectural configuration constraints; and (3) we present empirical results showing that the approximation algorithm can be used to derive architectural feature sets that are more than 90%+ optimal.