Feature Inclusion Probability

Description: Calculates the probability of each feature being included in a valid configuration.

Application: Useful for understanding the likelihood of feature presence across all valid configurations, aiding in feature prioritization and decision-making.

Example: Determining the probability of the ‘GPS’ feature being included in all valid configurations of a car model feature set.

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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("BDDFeatureInclusionProbability", "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
operation = dm.get_operation(bdd_model, 'BDDFeatureInclusionProbability')
# Execute the operation
operation.execute(bdd_model)
# Get and print the result
result = operation.get_result()
print(result)