RandSampFeatureModel is aimed at returning a sample of configurations following a uniform distribution.
Example:
java -jar randsampfm -e -c -s=100 --path=~/featuremodel.uvl
enumerates and counts the feasible configurations, whereas -s=100 returns 100 random configurations.
Credits : https://github.com/neominik/uvl-parser https://github.com/SundermannC/FeatureIDE