Pure Optional Features

Description: Returns the features with a feature inclusion probability of exactly 0.5, meaning they appear in exactly half of all valid configurations. These are the most unconstrained optional features — neither preferred nor avoided by the model’s constraints.

Application: Useful for identifying features that are freely optional without any structural bias, which can inform testing strategies and configuration design.

Example: Finding features in a home automation product line that are included in exactly half of all valid products.

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Code Examples

Python easy to use facade usage

from flamapy.interfaces.python.flamapy_feature_model import FLAMAFeatureModel
# Load the feature model
fm = FLAMAFeatureModel("path/to/feature/model")
result = fm.pure_optional_features()
print(result)

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