BDD plugin for flamapy

• BDD plugin for flamapy
  • Description
  • Requirements and Installation
  • Functionality and usage
    • Load a feature model and create the BDD
    • Save the BDD in a file
    • Analysis operations
  • Contributing to the BDD plugin

Description

This plugin supports Binary Decision Diagrams (BDDs) representations for feature models.

The plugin is based on flamapy and thus, it follows the same architecture:

The BDD plugin relies on the dd library to manipulate BDDs. The complete documentation of such library is available here.

The following is an example of feature model and its BDD using complemented arcs.

Requirements and Installation

• Python 3.9+
• This plugin depends on the flamapy core and on the Feature Model plugin.

pip install flamapy flamapy-fm flamapy-bdd

We have tested the plugin on Linux, but Windows is also supported.

Functionality and usage

The executable script test_bdd_metamodel.py serves as an entry point to show the plugin in action.

The following functionality is provided:

Load a feature model and create the BDD

from flamapy.metamodels.fm_metamodel.transformations.featureide_reader import FeatureIDEReader
from flamapy.metamodels.bdd_metamodel.transformations.fm_to_bdd import FmToBDD

# Load the feature model from FeatureIDE
feature_model = FeatureIDEReader('input_fms/featureide_models/pizzas.xml').transform()
# Create the BDD from the feature model
bdd_model = FmToBDD(feature_model).transform()

Save the BDD in a file

from flamapy.metamodels.bdd_metamodel.transformations.bdd_writer import BDDWriter, BDDDumpFormat
# Save the BDD as an image in PNG
BDDWriter(path='my_bdd.png',
          source_model=bdd_model,
          roots=[bdd_model.root],
          output_format=BDDDumpFormat.PNG).transform()

Formats supported: DDDMP_V3 ('dddmp'), DDDMP_V2 ('dddmp2'), PDF ('pdf'), PNG ('png'), SVG ('svg').

Analysis operations

• Products number

  Return the number of products (configurations):

  from flamapy.metamodels.bdd_metamodel.operations import BDDProductsNumber
  nof_products = BDDProductsNumber().execute(bdd_model).get_result()
  print(f'#Products: {nof_products}')

  or alternatively:

  from flamapy.metamodels.bdd_metamodel.operations import products_number
  nof_products = products_number(bdd_model)
  print(f'#Products: {nof_products}')

• Products

  Return the list of products (configurations):

  from flamapy.metamodels.bdd_metamodel.operations import BDDProducts
  list_products = BDDProducts().execute(bdd_model).get_result()
  for i, prod in enumerate(list_products):
      print(f'Product {i}: {[feat for feat in prod.elements if prod.elements[feat]]}')

  or alternatively:

  from flamapy.metamodels.bdd_metamodel.operations import products
  nof_products = products(bdd_model)
  for i, prod in enumerate(list_products):
      print(f'Product {i}: {[feat for feat in prod.elements if prod.elements[feat]]}')

• Sampling

  Return a sample of the given size of uniform random products (configurations) with or without replacement:

  from flamapy.metamodels.bdd_metamodel.operations import BDDSampling
  list_sample = BDDSampling(size=5, with_replacement=False).execute(bdd_model).get_result()
  for i, prod in enumerate(list_sample):
      print(f'Product {i}: {[feat for feat in prod.elements if prod.elements[feat]]}')

  or alternatively:

  from flamapy.metamodels.bdd_metamodel.operations import sample
  list_sample = sample(bdd_model, size=5, with_replacement=False)
  for i, prod in enumerate(list_sample):
      print(f'Product {i}: {[feat for feat in prod.elements if prod.elements[feat]]}')

• Product Distribution

  Return the number of products having a given number of features:

  from flamapy.metamodels.bdd_metamodel.operations import BDDProductDistributionBF
  dist = BDDProductDistributionBF().execute(bdd_model).get_result()
  print(f'Product Distribution: {dist}')

  or alternatively:

  from flamapy.metamodels.bdd_metamodel.operations import product_distribution
  dist = product_distribution(bdd_model)
  print(f'Product Distribution: {dist}')

• Feature Inclusion Probability

  Return the probability for a feature to be included in a valid product:

  from flamapy.metamodels.bdd_metamodel.operations import BDDFeatureInclusionProbabilityBF
  prob = BDDFeatureInclusionProbabilityBF().execute(bdd_model).get_result()
  for feat in prob.keys():
      print(f'{feat}: {prob[feat]}')

  or alternatively:

  from flamapy.metamodels.bdd_metamodel.operations import feature_inclusion_probability
  prob = feature_inclusion_probability(bdd_model)
  for feat in prob.keys():
      print(f'{feat}: {prob[feat]}')

All analysis operations support also a partial configuration as an additional argument, so the operation will return the result taking into account the given partial configuration. For example:

from flamapy.core.models import Configuration
# Create a partial configuration
elements = {'Pizza': True, 'Big': True}
partial_config = Configuration(elements)
# Calculate the number of products from the partial configuration
nof_products = BDDProductsNumber(partial_config).execute(bdd_model).get_result()
print(f'#Products: {nof_products}')

or alternatively:

nof_products = products(bdd_model, partial_config)
print(f'#Products: {nof_products}')

Contributing to the BDD plugin

To contribute in the development of this plugin:

1. Fork the repository into your GitHub account.
2. Clone the repository: git@github.com:<<username>>/bdd_metamodel.git
3. Create a virtual environment: python -m venv env
4. Activate the virtual environment: source env/bin/activate
5. Install the plugin dependencies: pip install flamapy flamapy-fm
6. Install the BDD plugin from the source code: pip install -e bdd_metamodel

Please try to follow the standards code quality to contribute to this plugin before creating a Pull Request:

• To analyze your Python code and output information about errors, potential problems, convention violations and complexity, pass the prospector with:

  make lint

• To analyze the static type checker for Python and find bugs, pass the Mypy:

  make mypy