RT Journal Article
JF Software Product Line Conference, International
YR 2008
VO 00
SP 22
TI Sample Spaces and Feature Models: There and Back Again
A1 Steven She,
A1 Andrzej Wasowski,
A1 Krzysztof Czarnecki,
K1 variability modeling
K1 feature models
K1 model-driven development
K1 configuration
K1 model mining
AB We present probabilistic feature models (PFMs) and illustrate their use by discussing modeling, mining and interactive configuration. PFMs are formalized as a set of formulas in a certain probabilistic logic. Such formulas can express both hard and soft constraints and have a well defined semantics by denoting a set of joint probability distributions over features. We show how PFMs can be mined from a given set of feature configurations using data mining techniques. Finally, we demonstrate how PFMs can be used in configuration in order to provide automated support for choice propagation based on both hard and soft constraints. We believe that these results constitute solid foundations for the construction of reverse engineering tools for software product lines and configurators using soft constraints.
PB IEEE Computer Society, [URL:http://www.computer.org]
LA English
DO 10.1109/SPLC.2008.49
LK http://doi.ieeecomputersociety.org/10.1109/SPLC.2008.49