Generate random feature model

Description: Generates a random synthetic feature model and returns it as a FeatureModel.

Application: Produces synthetic feature models for benchmarking analysis operations or testing tools, with a controlled size, number of constraints and language level.

Example: A reproducible 20-feature model with up to 5 cross-tree constraints, generated from a fixed seed and written to UVL.

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Code Examples

Python easy to use facade usage

from flamapy.interfaces.python.flamapy_feature_model import FLAMAFeatureModel
# Static method: no input model is needed
model = FLAMAFeatureModel.generate_random_feature_model(
    num_features=20, max_constraints=5, seed=42)
print(model)

Python flamapy framework usage

from flamapy.metamodels.fm_metamodel.operations import GenerateRandomFeatureModel
from flamapy.metamodels.fm_metamodel.transformations import UVLWriter
# Configure the generator
operation = GenerateRandomFeatureModel()
operation.set_num_features(20)
operation.set_max_constraints(5)
operation.set_seed(42)
# Other options: set_void(True) and set_language_level('boolean' | 'arithmetic' | 'type')
# Producer operation: it takes no input model
model = operation.execute().get_result()
# Save it as UVL
UVLWriter(path="random_model.uvl", source_model=model).transform()
print(model)

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Facade signature: generate_random_feature_model(num_features: int = 10, max_constraints: int = 3, seed: int = 0, void: bool = False, language_level: str = 'boolean')

Inputs:

name	type	required	default
num_features	int	no	10
max_constraints	int	no	3
seed	int	no	0
void	bool	no	False
language_level	str	no	‘boolean’

Default backend: none (runs on the feature model)

Implemented by:

plugin	exact	scale limit
fm_metamodel	yes	—