Sample Spaces and Feature Models: There and Back Again

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.