Average Branching Factor

Description: This calculates the average number of child features per parent feature in the feature model. It provides insight into the complexity of the model.

Application: Helps in understanding the model’s structure and identifying areas with high complexity.

Example: If a feature model has 10 parent features with a total of 30 child features, the average branching factor is 3.

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Code Examples

Command line usage

flamapy average_branching_factor "path/to/feature/model"

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")
# Average Branching Factor
avbf = fm.average_branching_factor()
print(avbf)

Python flamapy framework usage

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

Python flamapy framework ADVANCED usage

from flamapy.core.discover import DiscoverMetamodels
# Initiallize the dicover 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') 
# Call the operation, no automatic transformations will be needed
result = dm.use_operation(feature_model,'FMAverageBranchingFactor').get_result()
print(result)