Separation of Concerns in Feature Modeling: Support and Applications

Feature models (FMs) are a popular formalism for describing the commonality and variability of software product lines (SPLs) in terms of features. SPL development increasingly involves manipulating many large FMs, and thus scalable modular techniques that support compositional development of complex SPLs are required. In this paper, we describe how a set of complementary operators (aggregate, merge, slice) provides practical support for separation of concerns in feature modeling. We show how the combination of these operators can assist in tedious and error prone tasks such as automated correction of FM anomalies, update and extraction of FM views, reconciliation of FMs and reasoning about properties of FMs. For each task, we report on practical applications in different domains. We also present a technique that can efficiently decompose FMs with thousands of features and report our experimental results.