Domain feature models concisely express commonality and variability among variants of a software product line. For separation of concerns, e.g., due to legal restrictions, technical considerations, and business requirements, multi-view approaches restrict the configuration choices on feature models for different stakeholders. However, recent approaches lack a formalization for precise, yet flexible specifications of views that ensure every derivable configuration perspective to obey feature model semantics. Here, we introduce a novel approach for clustering feature models to create multi-perspectives. Such customized perspectives result from composition of multiple concern-relevant views. A structured view model is used to organize feature groups, whereat a feature can be contained in multiple views. We provide formalizations for view composition and guaranteed consistency of the resulting perspectives w.r.t. feature model semantics. Thereupon, an efficient algorithm to verify consistency for entire clusterings is provided. We present an implementation and evaluate our concepts on two case studies.

Domain feature models concisely express commonality and variability among variants of a software product line. For separation of concerns, e.g., due to legal restrictions, technical considerations, and business requirements, multi-view approaches restrict the configuration choices on feature models for different stakeholders. However, recent approaches lack a formalization for precise, yet flexible specifications of views that ensure every derivable configuration perspective to obey feature model semantics. Here, we introduce a novel approach for clustering feature models to create multi-perspectives. Such customized perspectives result from composition of multiple concern-relevant views. A structured view model is used to organize feature groups, whereat a feature can be contained in multiple views. We provide formalizations for view composition and guaranteed consistency of the resulting perspectives w.r.t. feature model semantics. Thereupon, an efficient algorithm to verify consistency for entire clusterings is provided. We present an implementation and evaluate our concepts on two case studies.