Random Sampling
The Random Sampling plugin allows random sampling for cardinality-based feature models. It generates a custom number of random valid configurations and outputs them into the console.
The Random Sampling plugin requires the model to be bound which means no infinite upper bounds as instance cardinalities are allowed. In case of an unbound model, you can use other plugins like the Big M plugin to replace infinte upper bounds with finite ones.
Usage
Import a cfm and generate 5 random samples for it:
The --num-samples parameter defaults to 1 if not specified.
In case of an unbound model, transform the model first with e.g. the Big M plugin.
python3 -m cfmtoolbox --import unbound.uvl --export bound.uvl apply-big-m
python3 -m cfmtoolbox --import bound.uvl random-sampling
Because the sampling algorithm uses non-determinism, it is recommended to limit the runtime of the command with a timeout of e.g. 5 seconds.
To store the sampling in a .json file, shell redirection can be used, as shown in the following example: