@Conference{DBLP:conf/fase/HaslingerLE13,
  author    = {Evelyn N. Haslinger and Roberto E. Lopez-Herrejon and Alexander Egyed},
  title     = {On Extracting Feature Models from Sets of Valid Feature Combinations.},
  booktitle = {Proceedings of the 16th International Conference on Fundamental Approaches to Software Engineering (FASE 2013), Rome, Italy},
  year      = {2013},
  editor    = {Vittorio Cortellessa and Daniel Varr},
  volume    = {7793},
  pages     = {53-67},
  publisher = {Springer},
  abstract  = {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.},
  bibsource = {{dblp computer science bibliography, https://dblp.org}},
  doi       = {10.1007/978-3-642-37057-1_5},
  file      = {:Conferences\\FASE 2013 - On Extracting Feature Models from Sets of Valid Feature Combinations\\On Extracting Feature Models from Sets of Valid Feature Combinations-preprint.pdf:PDF},
  keywords  = {FWF P21321, FWF M1421, EU IEF 254965},
}