Generating feature models from requirements: structural vs. functional perspectives

Adoption of SPLE techniques is challenging and expensive. Hence, automation in the adoption process is desirable, especially with respect to variability management. Different methods have been suggested for (semi-)automatically generating feature models from requirements or textual descriptions of products. However, while there are different ways to represent the same SPL in feature models, addressing different stakeholders' needs and preferences, existing methods usually follow fixed, predefined ways to generate feature models. As a result, the generated feature models may represent perspectives less relevant to the given tasks. In this paper we suggest an ontological approach that measures the semantic similarity, extracts variability, and automatically generates feature models that represent structural (objects-related) or functional (actions-related) perspectives. The stakeholders are able to control the perspective of the generated feature models, considering their needs and preferences for given tasks.