A taxonomy of variability realization techniques: Research Articles
Software—Practice & Experience
Software Product Line Engineering: Foundations, Principles and Techniques
Software Product Line Engineering: Foundations, Principles and Techniques
Reasoning about edits to feature models
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Proceedings of the 13th International Software Product Line Conference
Automated analysis of feature models 20 years later: A literature review
Information Systems
Reverse engineering feature models
Proceedings of the 33rd International Conference on Software Engineering
Reverse Engineering Feature Models from Programs' Feature Sets
WCRE '11 Proceedings of the 2011 18th Working Conference on Reverse Engineering
On extracting feature models from product descriptions
Proceedings of the Sixth International Workshop on Variability Modeling of Software-Intensive Systems
Feature models, grammars, and propositional formulas
SPLC'05 Proceedings of the 9th international conference on Software Product Lines
Efficient synthesis of feature models
Proceedings of the 16th International Software Product Line Conference - Volume 1
Reverse engineering feature models with evolutionary algorithms: an exploratory study
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
Feature model extraction from large collections of informal product descriptions
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
Comparing or configuring products: are we getting the right ones?
Proceedings of the Eighth International Workshop on Variability Modelling of Software-Intensive Systems
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Rather than developing individual systems, Software Product Line Engineering develops families of systems. The members of the software family are distinguished by the features they implement and Feature Models (FMs) are the de facto standard for defining which feature combinations are considered valid members. This paper presents an algorithm to automatically extract a feature model from a set of valid feature combinations, an essential development step when companies, for instance, decide to convert their existing product variations portfolio into a Software Product Line. We performed an evaluation on 168 publicly available feature models, with 9 to 38 features and up to 147456 feature combinations. From the generated feature combinations of each of these examples, we reverse engineered an equivalent feature model with a median performance in the low milliseconds.