Minimal corrections
Description: Returns the minimal corrections for a void feature model: each correction is a minimal set of relationships/cross-tree constraints whose removal makes the model satisfiable (FastDiag diagnoses enumerated via HSDAG), in human-readable form.
Application: Repairs a void feature model: each correction is a minimal set of relationships and constraints whose removal makes the model satisfiable again.
Example:
For a model where eCommerce requires Server but a constraint forbids it, the corrections are removing that constraint, or removing the relationship.
Code Examples
Command line usage
flamapy minimal_corrections "path/to/feature/model" --max_corrections 5
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")
# max_corrections bounds how many corrections are enumerated
result = fm.minimal_corrections(max_corrections=5)
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("PySATMinimalCorrections", "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 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 the diagnosis metamodel
sat_model = dm.use_transformation_m2m(feature_model, "pysat_diagnosis")
# Get the operation
operation = dm.get_operation(sat_model, 'PySATMinimalCorrections')
# Bound the number of corrections to enumerate
operation.set_max_corrections(5)
# Execute the operation
operation.execute(sat_model)
# Get and print the result
result = operation.get_result()
print(result)
Facade signature: minimal_corrections(self, max_corrections: int = None)
Inputs:
| name | type | required | default |
|---|---|---|---|
max_corrections |
int | no | None |
Default backend: pysat_diagnosis
Implemented by:
| plugin | exact | scale limit |
|---|---|---|
| pysat_diagnosis_metamodel | yes | — |