Metrics

Description: Returns a collection of structural metrics for the feature model, such as number of features, constraints, relations, abstract and concrete features, leaf features, and complexity indicators.

Application: Useful for getting a quick overview of the size and complexity of a feature model, comparing models, or feeding metrics into analysis pipelines.

Example: Obtaining the number of features and constraints in a software product line feature model.

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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.metrics()
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("FMMetrics", "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')
# Get the operation
operation = dm.get_operation(feature_model, 'FMMetrics')
# Execute the operation
operation.execute(feature_model)
# Get and print the result
result = operation.get_result()
print(result)