Plugins

The flamapy framework is designed to support automated analysis of feature models through a modular and extensible architecture. This flexibility is primarily achieved through the use of plugins, each encapsulating specific functionality, metamodels, and operations.

Table of contents

1. Concept of plugins
   1. Metamodel classes
   2. Operations
   3. Transformations
2. Architecture overview
   1. Core plugin
   2. Metamodel plugins
3. Available plugins
   1. flamapy_fw
   2. fm_metamodel
   3. pysat_metamodel
   4. bdd_metamodel
   5. z3_metamodel
   6. pysat_diagnosis_metamodel

Concept of plugins

Plugins in flamapy are the fundamental building blocks that extend the framework’s capabilities. Each plugin is designed to handle a specific type of analysis or transformation related to feature models. The plugins encapsulate:

Metamodel classes

Define the structure and constraints of the variability models.

Operations

Implement the actual analyses or computations that can be performed on the models.

Transformations

Handle the conversion between different model representations, crucial for leveraging various computational techniques.

Architecture overview

Core plugin

The core plugin provides the foundational elements of the framework, including interfaces for different transformations (Text-to-Model, Model-to-Text, Model-to-Model), and mechanisms for identifying and executing available operations.

Metamodel plugins

These plugins define the specific variability models and their associated operations and transformations. Examples include the feature model plugin, PySAT plugin, and BDD plugin.

Available plugins

flamapy offers a variety of plugins, each tailored to specific analysis needs and model types. Here are some of the key plugins available. Each plugin is provided in the Pypi package manager:

flamapy_fw

This is the main repository hosting the core components of flamapy . It includes the essential infrastructure for the framework, enabling plugin management, and execution of operations and transformations. Github

fm_metamodel

Hosts the concrete classes for feature models. It supports multiple feature model formats and provides various operations such as counting leaf features, estimating the number of valid configurations, and identifying core features. Github

pysat_metamodel

Provides the integration with PySAT, a meta-solver that enables the analysis of feature models using various SAT solvers. This plugin is essential for performing constraint satisfaction problems and other complex analyses. Github

bdd_metamodel

Supports Binary Decision Diagrams (BDDs) for the automated analysis of feature models. This plugin is particularly useful for operations that benefit from BDD techniques, such as counting valid configurations efficiently. Github

z3_metamodel

Integrates the Z3 SMT solver to enable analysis of feature models with typed attributes (Integer, Real, String). Supports satisfiability checking, configuration enumeration, attribute bound computation, and attribute optimization beyond Boolean logic. Github

pysat_diagnosis_metamodel

Extends the PySAT plugin with diagnosis capabilities. Given a configuration and a test case, it computes conflict sets (minimal inconsistent subsets of constraints) and diagnoses (minimal corrections). Built on the QuickXPlain and FastDiag algorithms. Github

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Table of contents