Sampling

Description: Generates a sample set of valid configurations from the feature model, providing a representative subset for analysis.

Application: Useful for performance testing, validation, and analysis without the need to process all possible configurations.

Example: Generating a sample set of valid smartphone configurations for testing purposes.

---

Code Examples

Python flamapy framework usage

from flamapy.core.discover import DiscoverMetamodels
# Initialize the discover metamodel
dm = DiscoverMetamodels()
# Call the operation. Transformations will be automatically executed
result = dm.use_operation_from_file("Sampling", "path/to/feature/model")
print(result)

Python flamapy framework ADVANCED usage

from flamapy.core.discover import DiscoverMetamodels
# Initialize the discover metamodel
dm = DiscoverMetamodels()
# Get the fm metamodel representation using the transformation required to get to the fm metamodel
feature_model = dm.use_transformation_t2m("path/to/feature/model", 'fm')
# Manually call a M2M transformation to BDD
bdd_model = dm.use_transformation_m2m(feature_model, "bdd")
# Get the operation, in this case, the bdd version
operation = dm.get_operation(bdd_model, 'BDDSampling')
# Execute the operation
operation.execute(bdd_model)
# Get and print the result
result = operation.get_result()
print(result)