In software product line (SPL) engineering, a software configuration can be obtained through a valid selection of features represented in a feature model (FM). With a strong separation between requirements and reusable components and a deep impact of high level choices on technical parts, determining and configuring an well-adapted software configuration is a long, cumbersome and error-prone activity. This paper presents a modeling process in which variability sources are separated in different FMs and inter-related by propositional constraints while consistency checking and propagation of variability choices are automated. We show how the variability requirements can be expressed and then refined at design time so that the set of valid software configurations to be considered at run time may be highly reduced. Software tools support the approach and some experimentations on a video surveillance SPL are also reported.

Feature models (FMs) are a popular formalism for describing the commonality and variability of software product lines (SPLs) in terms of features. As SPL development increasingly involves numerous large FMs, scalable modular techniques are required to manage their complexity. In this paper, we present a novel slicing technique that produces a projection of an FM, including constraints. The slicing allows SPL practitioners to find semantically meaningful decompositions of FMs and has been integrated into the FAMILIAR language.

Variability in software product lines is often expressed through feature models (FMs). To handle the complexity of increasingly larger FMs, we propose semantically meaningful decomposition support through a slicing operator. We describe how the slicing operator is integrated into the FAMILIAR environment and how it can be combined with other operators to support complex tasks over FMs in different case studies.

This work explores how model-driven engineering techniques can support the configuration of systems in domains presenting multiple variability factors. Video surveillance is a good candidate for which we have an extensive experience. Ultimately, we wish to automatically generate a software component assembly from an application specification, using model to model transformations. The challenge is to cope with variability both at the specification and at the implementation levels. Our approach advocates a clear separation of concerns. More precisely, we propose two feature models, one for task specification and the other for software components. The first model can be transformed into one or several valid component configurations through step-wise specialization. This paper outlines our approach, focusing on the two feature models and their relations. We particularly insist on variability and constraint modeling in order to achieve the mapping from domain variability to software variability through model transformations.