Python facade

Easy to use python facade

Using Python as a scripting language can be highly convenient for developing scripts and integrating our analysis into other processes. However, learning a framework as complex as flamapy can initially seem overwhelming. To alleviate this, flamapy provides a straightforward method for executing the most common operations in Python.

After installing flamapy we can use the tool as easy as

from flamapy.interfaces.python.flamapy_feature_model import FLAMAFeatureModel
# Load the feature model
fm = FLAMAFeatureModel("path/to/feature/model")
# Pass backend="sat" | "bdd" | "z3" to choose the analysis plugin (use "sat" to force pysat in WASM environments)
result = fm.satisfiable() 
print(result)

Every operation that more than one plugin implements accepts backend=: a plugin name, or "auto" to let the query planner pick the cheapest plugin that can handle the model. After a call, fm.last_result tells which plugin ran it. See Choosing the backend (query planner).

As you might notice, this interface is heavily inspired in the way FAMILIAR worked in the java 6 years

Supported operations

You can find a comprehensive list of the supported operations for the command line and code snipets if you want to run them:

• All Feature Bounds
• Atomic Sets
• Attribute Optimization
• Average Branching Factor
• Backbone
• Commonality
• Configuration conflict
• Configuration repair
• Configurations
• Number of Configurations
• Configurations With N Features
• Conflict
• Core Features
• Count Leafs
• Coverage
• Covering array
• Dead Features
• Diagnosis
• Estimated number of configurations
• Explain dead feature
• Explain false optional
• Explain void model
• False Optional Features
• Feature Ancestors
• Feature Bounds
• Feature explanation
• Feature Inclusion Probability
• Filter
• Generate Random Attribute
• Generate random feature model
• Homogeneity
• Language Level
• Leaf Features
• Max Depth
• Metrics
• Minimal corrections
• Minimum configuration
• Product distribution
• Pure Optional Features
• Sample reduction
• Sampling
• Satisfiable
• Satisfiable Configuration
• T-wise sampling
• Unique Features
• Variability
• Variant Features
• Variation Points