Satisfiable

Description: Checks whether a given configuration of features is valid according to the constraints defined in the feature model.

Application: Ensures that the selected combination of features can form a valid product.

Example: Validating a configuration where all required features are included, and no constraints are violated.

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Code Examples

Command line usage

flamapy satisfiable "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")
# This method could be called with the param  with_sat: bool = True if you want to force pysat (useful for WASM enviroments) 
result = fm.satisfiable() 
print(result)

Python flamapy framework usage

from flamapy.core.discover import DiscoverMetamodels
# Initiallize the dicover metamodel
dm = DiscoverMetamodels()
# Call the operation. Transformations will be automatically executed
# Use BDDConfigurations if you want to rely on BDD solver
result = dm.use_operation_from_file("PySATSatisfiable","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') 
# Manually call a M2M transformation to Pysat
sat_model = dm.use_transformation_m2m(feature_model,"pysat")
# Get the operation
operation = dm.get_operation(sat_model,'PySATSatisfiable')
# Execute the operation
operation.execute(sat_model)
# Get and print the result
result = operation.get_result()
print(result)