Diverso Lab

FaMa Framework: Automated Analyses of Feature Models

Main features of the framework

We present a Python-based AAFM framework that takes into consideration previous AAFM tool designs and enables multi-solver and multi-metamodel support for the integration of AAFM tooling on the Python ecosystem

Easy to extend by enabling the creation of new plugins following a semi-automatic generator approach.

Core operations

Valid Model

This operation checks if the model is valid withrespect to its semantics.

Valid Configuration.

This operation takes a model and a partial configuration and checks if the configuration is correct or not.

Valid Product.

This operation takes a product and checks its validity on top of the selected model.

All products.

This operation prints out the list of valid products from a feature model

Dead Features.

This operation detects those features that cannot be present in any valid configuration

Core Features.

This operation returns the features present in all products

Error detection.

This operation returns a set of errors in a model.

Error diagnosis.

This operation returns the possible solutions for the errors present in a model

Try!

You can try our framework here

Authors