Configuration of mechatronic multi product lines
Proceedings of the 3rd international workshop on Variability & Composition
Developing platforms for multiple software product lines
Proceedings of the 16th International Software Product Line Conference - Volume 1
Dynamic configuration management of cloud-based applications
Proceedings of the 16th International Software Product Line Conference - Volume 2
Towards modeling and analyzing variability in evolving software ecosystems
Proceedings of the Seventh International Workshop on Variability Modelling of Software-intensive Systems
Integrating heterogeneous variability modeling approaches with invar
Proceedings of the Seventh International Workshop on Variability Modelling of Software-intensive Systems
FAMILIAR: A domain-specific language for large scale management of feature models
Science of Computer Programming
Generation of conjoint domain models for system-of-systems
Proceedings of the 12th international conference on Generative programming: concepts & experiences
Variability management in an unaware software product line company: an experience report
Proceedings of the Eighth International Workshop on Variability Modelling of Software-Intensive Systems
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In industrial settings, products are rarely developed by one organization alone. Software vendors and suppliers typically maintain their own product lines, which can contribute to a larger (multi) product line. The teams involved often use different approaches and tools to manage the variability of their systems. It is unrealistic to assume that all participating units can use a standardized and prescribed variability modeling technique. The configuration of products based on several models in different notations and with different semantics is not well supported by existing approaches. In this paper we present an integrative approach that provides a unified perspective to users configuring products in multi product line environments, regardless of the different modeling methods and tools used internally. We also present a technical infrastructure and a prototypic implementation based on Web Services. We show the feasibility of the approach and its implementation by using it with two different variability modeling approaches (one feature-based and one decision-oriented approach) on an example derived from industrial experience.