The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Towards Distributed Configuration
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
FeaturePlugin: feature modeling plug-in for Eclipse
eclipse '04 Proceedings of the 2004 OOPSLA workshop on eclipse technology eXchange
Automated analysis of feature models: challenges ahead
Communications of the ACM - Software product line
Feature models, grammars, and propositional formulas
SPLC'05 Proceedings of the 9th international conference on Software Product Lines
Efficient compilation techniques for large scale feature models
GPCE '08 Proceedings of the 7th international conference on Generative programming and component engineering
Decision-making coordination and efficient reasoning techniques for feature-based configuration
Science of Computer Programming
Automated analysis of feature models 20 years later: A literature review
Information Systems
Proceedings of the 2013 International Conference on Software Engineering
Supporting multiple perspectives in feature-based configuration
Software and Systems Modeling (SoSyM)
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In Software Product Lines (SPLs), product configuration is a decision-making process in which a group of stakeholders choose features for a product. Unfortunately, current configuration technology is essentially single-user-based in which user requirements are interpreted and translated into configuration decisions by a single role commonly referred to as the product manager. This process can be error-prone and time-consuming as it commonly requires back-and-forth interactions between the product manager and the stakeholders to cope with decision conflicts. In this paper, we propose an approach to Collaborative Product Configuration (CPC) that aims at providing effective support for coordinating teamwork decision-making in the context of product configuration. The approach builds on well-known concepts in the SPL arena such as feature models. The contributions of the paper include the CPC approach and the illustration of its application in a real-world product line.