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flamapy-bdd-1.0.1.dev0
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bdd-plugin for the automated analysis of feature models
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | flamapy-bdd-1.0.1.dev0 |
| نام | flamapy-bdd |
| نسخه کتابخانه | 1.0.1.dev0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Flamapy |
| ایمیل نویسنده | flamapy@us.es |
| آدرس صفحه اصلی | https://github.com/flamapy/bdd_metamodel |
| آدرس اینترنتی | https://pypi.org/project/flamapy-bdd/ |
| مجوز | - |
# BDD plugin for flamapy
- [BDD plugin for flamapy](#bdd-plugin-for-flamapy)
- [Description](#description)
- [Requirements and Installation](#requirements-and-installation)
- [Functionality and usage](#functionality-and-usage)
- [Load a feature model and create the BDD](#load-a-feature-model-and-create-the-bdd)
- [Save the BDD in a file](#save-the-bdd-in-a-file)
- [Analysis operations](#analysis-operations)
- [Contributing to the BDD plugin](#contributing-to-the-bdd-plugin)
## Description
This plugin supports Binary Decision Diagrams (BDDs) representations for feature models.
The plugin is based on [flamapy](https://github.com/flamapy/core) and thus, it follows the same architecture:
<p align="center">
<img width="750" src="doc/bdd_plugin.png">
</p>
The BDD plugin relies on the [dd](https://github.com/tulip-control/dd) library to manipulate BDDs.
The complete documentation of such library is available [here](https://github.com/tulip-control/dd/blob/main/doc.md).
The following is an example of feature model and its BDD using complemented arcs.
<p align="center">
<img width="750" src="doc/fm_example.png">
</p>
<p align="center">
<img width="750" src="doc/bdd_example.svg">
</p>
## Requirements and Installation
- Python 3.9+
- This plugin depends on the [flamapy core](https://github.com/flamapy/core) and on the [Feature Model plugin](https://github.com/flamapy/fm_metamodel).
```
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](https://github.com/flamapy/bdd_metamodel/blob/master/tests/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
```python
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
```python
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):
```python
from flamapy.metamodels.bdd_metamodel.operations import BDDProductsNumber
nof_products = BDDProductsNumber().execute(bdd_model).get_result()
print(f'#Products: {nof_products}')
```
or alternatively:
```python
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):
```python
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:
```python
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:
```python
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:
```python
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:
```python
from flamapy.metamodels.bdd_metamodel.operations import BDDProductDistributionBF
dist = BDDProductDistributionBF().execute(bdd_model).get_result()
print(f'Product Distribution: {dist}')
```
or alternatively:
```python
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:
```python
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:
```python
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:
```python
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:
```python
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`
نیازمندی
| مقدار | نام |
|---|---|
| ~=1.0.1 | flamapy |
| ~=1.0.1 | flamapy-fm |
| >=0.5.6graphviz~=0.20 | dd |
| - | pytest |
| - | pytest-mock |
| - | prospector |
| - | mypy |
| - | coverage |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.9 | Python |
نحوه نصب
نصب پکیج whl flamapy-bdd-1.0.1.dev0:
pip install flamapy-bdd-1.0.1.dev0.whl
نصب پکیج tar.gz flamapy-bdd-1.0.1.dev0:
pip install flamapy-bdd-1.0.1.dev0.tar.gz