Abstract
ContextA software product line is a family of software systems that share some common features but also have significant variabilities. A feature model is a variability modeling artifact, which represents differences among software products with respect to the variability relationships among their features. Having a feature model along with a reference model developed in the domain engineering lifecycle, a concrete product of the family is derived by binding the variation points in the feature model (called configuration process) and by instantiating the reference model. ObjectiveIn this work we address the feature model configuration problem and propose a framework to automatically select suitable features that satisfy both the functional and non-functional preferences and constraints of stakeholders. Additionally, interdependencies between various non-functional properties are taken into account in the framework. MethodThe proposed framework combines Analytical Hierarchy Process (AHP) and Fuzzy Cognitive Maps (FCM) to compute the non-functional properties weights based on stakeholders’ preferences and interdependencies between non-functional properties. Afterwards, Hierarchical Task Network (HTN) planning is applied to find the optimal feature model configuration. ResultOur approach improves state-of-art of feature model configuration by considering positive or negative impacts of the features on non-functional properties, the stakeholders’ preferences, and non-functional interdependencies. The approach presented in this paper extends earlier work presented in [1] from several distinct perspectives including mechanisms handling interdependencies between non-functional properties, proposing a novel tooling architecture, and offering visualization and interaction techniques for representing functional and non-functional aspects of feature models. Conclusionour experiments show the scalability of our configuration approach when considering both functional and non-functional requirements of stakeholders.
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