This UVL generator depends on uvl-smt for reasoning and java-fm-metamodel for managing the model objects.
UVL-SMT depends on z3 binaries which need to be correctly linked when running the generator. Check their README for instructions.
Currently, the generator has two main modes. Generating random models with default bounds or generating models with more specialized properties specified as json file.
You can find examples for json files in input_examples.
The main method can be found in the Runner.java class.
For random models, use the options --boundedRandom --n <numberOfModels>.
For using a file, use --file <path/to/file>.
The .json contains various configuration options that enable the user to control structural properties of the generated feature models. The example below generates 10 feature models with 500-600 features and 50--100 constraints. Furthermore, the groups in the tree hierarchy are 10% optional, 20% mandatory and so on. An attribute price is attached to features with a 50% chance and with values between 10 and 1000.
{
"general" : {
"numberModels" : 10,
"seed" : 42,
"ensureSAT" : true,
"generatorVersion" : "0.1"
},
"tree" : {
"features" : {
"number" : [500,600],
"distribution" : {
"boolean" : 0.7,
"integer" : 0.2,
"real" : 0.1,
"string" : 0
},
"cardinality" : {
"min" : [1,2],
"max" : 5,
"attachProbability" : 0.2
}
},
"maxTreeDepth" : 5,
"groups" : {
"distribution": {
"optional" : 0.1,
"mandatory" : 0.2,
"alternative" : 0.5,
"or" : 0.2,
"groupCardinality" : 0
}
}
},
"constraints" : {
"number" : [50,100],
"ecr" : 0.8,
"variablesPerConstraint" : [2,15],
"distribution" : {
"boolean" : 0.9,
"numeric" : 0.07,
"aggregate" : 0.03,
"string" : 0
}
},
"attributes" : [
{
"name" : "Price",
"value" : [10,1000],
"attachProbability" : 0.5,
"useInConstraints" : true
}
]
}