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Official flamapy documentation, the cutting-edge Python-based tool for Automatic Analysis of Feature Models (AAFM). |
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Introducing {% include flamapy.html %}, the cutting-edge Python-based tool for Automated Analysis of Feature Models (AAFM) using UVL and more. {% include flamapy.html %} revolutionizes feature model analysis by integrating the strengths of previous AAFM tools with advanced multi-solver and multi-metamodel capabilities.
Get started now{: .btn .btn-primary .fs-5 .mb-4 .mb-md-0 .mr-2 } View it on GitHub{: .btn .fs-5 .mb-4 .mb-md-0 }
- Plugin Generator: Simplifies the process of creating new plugins with a semi-automatic generator, making customization and expansion straightforward.
- Variability modelling in the wild: Initially supports cardinality-based feature models, with the flexibility to easily incorporate other types like attributed feature models.
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PySAT Integration: Utilizes the PySAT metasolver, offering access to more than ten distinct solvers. This diversity allows for optimal solution finding across various complex scenarios.
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BDD Integration: Utilizes the CU-BDD metasolver, offering efficient variability model analysis for some operations.
- Easy-to-use python facade: Designed with capabilities to analyse modes in Python with just a line of code.
- Command line direct use: Easy to integrate in any ecosystem.
- WASM support: Run analysis in your browser. Currently, both {% include flamapy.html %} and PySAT are WASM compatible. Enable analysis with 0 configuration process.
- REST / SWAGGER available: Integrate the tool in yours by means of a robust backend Rest API.
You can find all set operations here
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Detailed changes for each release are documented in the release notes.
When contributing to this repository, please first read [contributing].
[getting-started]: {% link getting-started.md %}
[contributing]: {% link contributing/contributing.md %}