/// <summary>
/// Try to find a configuration with low weight.
/// </summary>
/// <param name="sortedRanking">A list of binary options and their weight ordered by their weight.</param>
/// <param name="cache">A sat solver cache instance that already contains the constraints of
/// size and disallowed features.</param>
/// <param name="vm">The variability model of the given system.</param>
/// <returns>A configuration that has a small weight.</returns>
public static List <BinaryOption> getSmallWeightConfig(List <KeyValuePair <List <BinaryOption>, int> > sortedRanking,
Z3Cache cache, VariabilityModel vm)
{
KeyValuePair <List <BinaryOption>, int>[] ranking = sortedRanking.ToArray();
Microsoft.Z3.Solver solver = cache.GetSolver();
Context z3Context = cache.GetContext();
for (int i = 0; i < ranking.Length; i++)
{
List <BinaryOption> candidates = ranking[i].Key;
solver.Push();
solver.Assert(forceFeatures(candidates, z3Context, cache.GetOptionToTermMapping()));
if (solver.Check() == Status.SATISFIABLE)
{
Model model = solver.Model;
solver.Pop();
return(Z3VariantGenerator.RetrieveConfiguration(cache.GetVariables(), model,
cache.GetTermToOptionMapping()));
}
solver.Pop();
}
return(null);
}
public List <BinaryOption> GenerateConfigurationFromBucket(VariabilityModel vm, int numberSelectedFeatures, Dictionary <List <BinaryOption>, int> featureWeight, Configuration lastSampledConfiguration)
{
if (_z3Cache == null)
{
_z3Cache = new Dictionary <int, Z3Cache>();
}
List <KeyValuePair <List <BinaryOption>, int> > featureRanking;
if (featureWeight != null)
{
featureRanking = featureWeight.ToList();
featureRanking.Sort((first, second) => first.Value.CompareTo(second.Value));
}
else
{
featureRanking = new List <KeyValuePair <List <BinaryOption>, int> >();
}
List <BoolExpr> variables = null;
Dictionary <BoolExpr, BinaryOption> termToOption = null;
Dictionary <BinaryOption, BoolExpr> optionToTerm = null;
Tuple <Context, BoolExpr> z3Tuple;
Context z3Context;
Microsoft.Z3.Solver solver;
// Reuse the solver if it is already in the cache
if (this._z3Cache.Keys.Contains(numberSelectedFeatures))
{
Z3Cache cache = this._z3Cache[numberSelectedFeatures];
z3Context = cache.GetContext();
solver = cache.GetSolver();
variables = cache.GetVariables();
termToOption = cache.GetTermToOptionMapping();
optionToTerm = cache.GetOptionToTermMapping();
if (lastSampledConfiguration != null)
{
// Add the previous configurations as constraints
solver.Assert(Z3Solver.NegateExpr(z3Context, Z3Solver.ConvertConfiguration(z3Context, lastSampledConfiguration.getBinaryOptions(BinaryOption.BinaryValue.Selected), optionToTerm, vm)));
// Create a new backtracking point for the next run
solver.Push();
}
}
else
{
z3Tuple = Z3Solver.GetInitializedBooleanSolverSystem(out variables, out optionToTerm, out termToOption, vm, this.henard);
z3Context = z3Tuple.Item1;
BoolExpr z3Constraints = z3Tuple.Item2;
solver = z3Context.MkSolver();
// TODO: The following line works for z3Solver version >= 4.6.0
//solver.Set (RANDOM_SEED, z3RandomSeed);
Params solverParameter = z3Context.MkParams();
solverParameter.Add(RANDOM_SEED, z3RandomSeed);
solver.Parameters = solverParameter;
solver.Assert(z3Constraints);
if (lastSampledConfiguration != null)
{
// Add the previous configurations as constraints
solver.Assert(Z3Solver.NegateExpr(z3Context, Z3Solver.ConvertConfiguration(z3Context, lastSampledConfiguration.getBinaryOptions(BinaryOption.BinaryValue.Selected), optionToTerm, vm)));
}
// The goal of this method is, to have an exact number of features selected
// Therefore, initialize an integer array with the value '1' for the pseudo-boolean equal function
int[] neutralWeights = new int[variables.Count];
for (int i = 0; i < variables.Count; i++)
{
neutralWeights[i] = 1;
}
solver.Assert(z3Context.MkPBEq(neutralWeights, variables.ToArray(), numberSelectedFeatures));
// Create a backtracking point before adding the optimization goal
solver.Push();
this._z3Cache[numberSelectedFeatures] = new Z3Cache(z3Context, solver, variables, optionToTerm, termToOption);
}
// Check if there is still a solution available by finding the first satisfiable configuration
if (solver.Check() == Status.SATISFIABLE)
{
Model model = solver.Model;
List <BinaryOption> possibleSolution = RetrieveConfiguration(variables, model, termToOption);
// Disable finding a configuration where the least frequent feature/feature combinations are selected
// if no featureWeight is given.
List <BinaryOption> approximateOptimal = null;
if (featureRanking.Count != 0)
{
approximateOptimal = WeightMinimizer
.getSmallWeightConfig(featureRanking, this._z3Cache[numberSelectedFeatures], vm);
}
if (approximateOptimal == null)
{
return(possibleSolution);
}
else
{
return(approximateOptimal);
}
}
else
{
return(null);
}
}
public List <BinaryOption> GenerateConfigurationFromBucket(VariabilityModel vm, int numberSelectedFeatures, Dictionary <List <BinaryOption>, int> featureWeight, List <BinaryOption> desiredOptions)
{
if (_z3Cache == null)
{
_z3Cache = new Dictionary <int, Z3Cache>();
}
List <KeyValuePair <List <BinaryOption>, int> > featureRanking;
if (featureWeight != null)
{
featureRanking = featureWeight.OrderBy(pair => pair.Value).ToList();
}
else
{
featureRanking = new List <KeyValuePair <List <BinaryOption>, int> >();
}
List <BoolExpr> variables = null;
Dictionary <BoolExpr, BinaryOption> termToOption = null;
Microsoft.Z3.Solver solver;
if (!this._z3Cache.Keys.Contains(numberSelectedFeatures))
{
InitializeZ3Cache(vm, numberSelectedFeatures);
}
Z3Cache cache = this._z3Cache[numberSelectedFeatures];
solver = cache.GetSolver();
variables = cache.GetVariables();
termToOption = cache.GetTermToOptionMapping();
Dictionary <BinaryOption, BoolExpr> optionToTerm = cache.GetOptionToTermMapping();
Context z3Context = cache.GetContext();
solver.Push();
if (desiredOptions != null)
{
foreach (BinaryOption binaryOption in desiredOptions)
{
solver.Add(optionToTerm[binaryOption]);
}
}
// Check if there is still a solution available by finding the first satisfiable configuration
if (solver.Check() == Status.SATISFIABLE)
{
Model model = solver.Model;
List <BinaryOption> possibleSolution = RetrieveConfiguration(variables, model, termToOption);
// Disable finding a configuration where the least frequent feature/feature combinations are selected
// if no featureWeight is given.
List <BinaryOption> approximateOptimal = null;
if (featureRanking.Count != 0)
{
approximateOptimal = WeightMinimizer
.getSmallWeightConfig(featureRanking, this._z3Cache[numberSelectedFeatures], vm);
}
solver.Pop();
if (approximateOptimal == null)
{
return(possibleSolution);
}
else
{
return(approximateOptimal);
}
}
else
{
solver.Pop();
return(null);
}
}