private bool Solve_Brtrue(CflowStack stack)
{
var val1 = stack.Pop();
if (val1 is BitVecExpr)
{
FinalExpr = ctx.MkEq(val1 as BitVecExpr, ctx.MkBV(0, 32));
if ((val1 as BitVecExpr).Simplify().IsNumeral)
{
Microsoft.Z3.Solver s = ctx.MkSolver();
s.Assert(FinalExpr);
if (s.Check() == Status.UNSATISFIABLE)
{
this.PopPushedArgs(1);
block.ReplaceBccWithBranch(true);
return(true);
}
else
{
this.PopPushedArgs(1);
block.ReplaceBccWithBranch(false);
return(true);
}
}
}
return(false);
}
/// <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);
}
private bool CheckBranch(Microsoft.Z3.Solver s1, Microsoft.Z3.Solver s2)
{
if (s1.Check() == Status.UNSATISFIABLE)
{
// opaque predicate not taken
this.PopPushedArgs(2);
block.ReplaceBccWithBranch(false);
return(true);
}
else if (s2.Check() == Status.UNSATISFIABLE)
{
// opaque predicate taken
this.PopPushedArgs(2);
block.ReplaceBccWithBranch(true);
return(true);
}
else
{
return(false);
}
}
public bool SolveBranchAssisted(Context ctx, BoolExpr expr)
{
Instruction instr = block.LastInstr.Instruction;
if (instr.OpCode.Code == Code.Brfalse || instr.OpCode.Code == Code.Brfalse_S)
{
Microsoft.Z3.Solver s = ctx.MkSolver();
s.Assert(expr);
if (s.Check() == Status.SATISFIABLE)
{
this.PopPushedArgs(1);
block.ReplaceBccWithBranch(true);
return(true);
}
else
{
this.PopPushedArgs(1);
block.ReplaceBccWithBranch(false);
return(true);
}
}
else if (instr.OpCode.Code == Code.Brtrue || instr.OpCode.Code == Code.Brtrue_S)
{
Microsoft.Z3.Solver s = ctx.MkSolver();
s.Assert(expr);
if (s.Check() == Status.UNSATISFIABLE)
{
this.PopPushedArgs(1);
block.ReplaceBccWithBranch(true);
return(true);
}
else
{
this.PopPushedArgs(1);
block.ReplaceBccWithBranch(false);
return(true);
}
}
else
{
Microsoft.Z3.Solver s1 = ctx.MkSolver();
Microsoft.Z3.Solver s2 = ctx.MkSolver();
s1.Add(expr);
s2.Add(ctx.MkNot(expr));
return(this.CheckBranch(s1, s2));
}
}
/// <summary>
/// Generates all valid combinations of all configuration options in the given model.
/// </summary>
/// <param name="vm">the variability model containing the binary options and their constraints</param>
/// <param name="optionsToConsider">the options that should be considered. All other options are ignored</param>
/// <returns>Returns a list of <see cref="Configuration"/></returns>
public List <Configuration> GenerateAllVariants(VariabilityModel vm, List <ConfigurationOption> optionsToConsider)
{
List <Configuration> allConfigurations = new List <Configuration>();
List <BoolExpr> variables;
Dictionary <BoolExpr, BinaryOption> termToOption;
Dictionary <BinaryOption, BoolExpr> optionToTerm;
Tuple <Context, BoolExpr> z3Tuple = Z3Solver.GetInitializedBooleanSolverSystem(out variables, out optionToTerm, out termToOption, vm, this.henard);
Context z3Context = z3Tuple.Item1;
BoolExpr z3Constraints = z3Tuple.Item2;
Microsoft.Z3.Solver 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);
while (solver.Check() == Status.SATISFIABLE)
{
Model model = solver.Model;
List <BinaryOption> binOpts = RetrieveConfiguration(variables, model, termToOption, optionsToConsider);
Configuration c = new Configuration(binOpts);
// Check if the non-boolean constraints are satisfied
if (vm.configurationIsValid(c) && !VariantGenerator.IsInConfigurationFile(c, allConfigurations) && VariantGenerator.FulfillsMixedConstraints(c, vm))
{
allConfigurations.Add(c);
}
solver.Push();
solver.Assert(Z3Solver.NegateExpr(z3Context, Z3Solver.ConvertConfiguration(z3Context, binOpts, optionToTerm, vm)));
}
solver.Push();
solver.Pop(Convert.ToUInt32(allConfigurations.Count() + 1));
return(allConfigurations);
}
public bool checkConfigurationSAT(List <BinaryOption> config, VariabilityModel vm, bool partialConfiguration)
{
List <BoolExpr> variables;
Dictionary <BoolExpr, BinaryOption> termToOption;
Dictionary <BinaryOption, BoolExpr> optionToTerm;
Tuple <Context, BoolExpr> z3Tuple = Z3Solver.GetInitializedBooleanSolverSystem(out variables, out optionToTerm, out termToOption, vm, false);
Context z3Context = z3Tuple.Item1;
BoolExpr z3Constraints = z3Tuple.Item2;
List <BoolExpr> constraints = new List <BoolExpr>();
Microsoft.Z3.Solver solver = z3Context.MkSolver();
solver.Assert(z3Constraints);
solver.Assert(Z3Solver.ConvertConfiguration(z3Context, config, optionToTerm, vm, partialConfiguration));
if (solver.Check() == Status.SATISFIABLE)
{
return(true);
}
return(false);
}
/// <summary>
/// Generates all valid combinations of all configuration options in the given model.
/// </summary>
/// <param name="vm">the variability model containing the binary options and their constraints</param>
/// <param name="optionsToConsider">the options that should be considered. All other options are ignored</param>
/// <returns>Returns a list of <see cref="Configuration"/></returns>
public List <Configuration> GenerateAllVariants(VariabilityModel vm, List <ConfigurationOption> optionsToConsider)
{
List <Configuration> allConfigurations = new List <Configuration>();
List <Expr> variables;
Dictionary <Expr, ConfigurationOption> termToOption;
Dictionary <ConfigurationOption, Expr> optionToTerm;
Tuple <Context, BoolExpr> z3Tuple = Z3Solver.GetInitializedSolverSystem(out variables, out optionToTerm, out termToOption, vm);
Context z3Context = z3Tuple.Item1;
BoolExpr z3Constraints = z3Tuple.Item2;
Microsoft.Z3.Solver solver = z3Context.MkSolver();
solver.Set(RANDOM_SEED, z3RandomSeed);
solver.Assert(z3Constraints);
while (solver.Check() == Status.SATISFIABLE)
{
Model model = solver.Model;
Tuple <List <BinaryOption>, Dictionary <NumericOption, double> > confOpts = RetrieveConfiguration(variables, model, termToOption, optionsToConsider);
Configuration c = new Configuration(confOpts.Item1, confOpts.Item2);
// Check if the non-boolean constraints are satisfied
bool configIsValid = vm.configurationIsValid(c);
bool isInConfigurationFile = !VariantGenerator.IsInConfigurationFile(c, allConfigurations);
bool fulfillsMixedConstraintrs = VariantGenerator.FulfillsMixedConstraints(c, vm);
if (configIsValid && isInConfigurationFile && fulfillsMixedConstraintrs)
{
allConfigurations.Add(c);
}
solver.Push();
solver.Assert(Z3Solver.NegateExpr(z3Context, Z3Solver.ConvertConfiguration(z3Context, confOpts.Item1, optionToTerm, vm, numericValues: confOpts.Item2)));
}
solver.Push();
solver.Pop(Convert.ToUInt32(allConfigurations.Count() + 1));
return(allConfigurations);
}
public bool checkConfigurationSAT(Configuration c, VariabilityModel vm, bool partialConfiguration = false)
{
List <Expr> variables;
Dictionary <Expr, ConfigurationOption> termToOption;
Dictionary <ConfigurationOption, Expr> optionToTerm;
Tuple <Context, BoolExpr> z3Tuple = Z3Solver.GetInitializedSolverSystem(out variables, out optionToTerm, out termToOption, vm);
Context z3Context = z3Tuple.Item1;
BoolExpr z3Constraints = z3Tuple.Item2;
List <Expr> constraints = new List <Expr>();
Microsoft.Z3.Solver solver = z3Context.MkSolver();
solver.Assert(z3Constraints);
solver.Assert(Z3Solver.ConvertConfiguration(z3Context, c.getBinaryOptions(BinaryOption.BinaryValue.Selected), optionToTerm, vm, partialConfiguration, c.NumericOptions));
if (solver.Check() == Status.SATISFIABLE)
{
return(true);
}
return(false);
}
/// <summary>
/// Demonstrate how to use <code>Push</code>and <code>Pop</code>to
/// control the size of models.
/// </summary>
/// <remarks>Note: this test is specialized to 32-bit bitvectors.</remarks>
public static void CheckSmall(Context ctx, Solver solver, BitVecExpr[] to_minimize)
{
Sort bv32 = ctx.MkBitVecSort(32);
int num_Exprs = to_minimize.Length;
UInt32[] upper = new UInt32[num_Exprs];
UInt32[] lower = new UInt32[num_Exprs];
BitVecExpr[] values = new BitVecExpr[num_Exprs];
for (int i = 0; i < upper.Length; ++i)
{
upper[i] = UInt32.MaxValue;
lower[i] = 0;
}
bool some_work = true;
int last_index = -1;
UInt32 last_upper = 0;
while (some_work)
{
solver.Push();
bool check_is_sat = true;
while (check_is_sat && some_work)
{
// Assert all feasible bounds.
for (int i = 0; i < num_Exprs; ++i)
{
solver.Assert(ctx.MkBVULE(to_minimize[i], ctx.MkBV(upper[i], 32)));
}
check_is_sat = Status.SATISFIABLE == solver.Check();
if (!check_is_sat)
{
if (last_index != -1)
{
lower[last_index] = last_upper + 1;
}
break;
}
Console.WriteLine("{0}", solver.Model);
// narrow the bounds based on the current model.
for (int i = 0; i < num_Exprs; ++i)
{
Expr v = solver.Model.Evaluate(to_minimize[i]);
UInt64 ui = ((BitVecNum)v).UInt64;
if (ui < upper[i])
{
upper[i] = (UInt32)ui;
}
Console.WriteLine("{0} {1} {2}", i, lower[i], upper[i]);
}
// find a new bound to add
some_work = false;
last_index = 0;
for (int i = 0; i < num_Exprs; ++i)
{
if (lower[i] < upper[i])
{
last_upper = (upper[i] + lower[i]) / 2;
last_index = i;
solver.Assert(ctx.MkBVULE(to_minimize[i], ctx.MkBV(last_upper, 32)));
some_work = true;
break;
}
}
}
solver.Pop();
}
}
/// <summary>
/// This method searches for a corresponding methods in the dynamically loaded assemblies and calls it if found. It prefers due to performance reasons the Microsoft Solver Foundation implementation.
/// </summary>
/// <param name="config">The (partial) configuration which needs to be expaned to be valid.</param>
/// <param name="vm">Variability model containing all options and their constraints.</param>
/// <param name="minimize">If true, we search for the smallest (in terms of selected options) valid configuration. If false, we search for the largest one.</param>
/// <param name="unWantedOptions">Binary options that we do not want to become part of the configuration. Might be part if there is no other valid configuration without them.</param>
/// <returns>The valid configuration (or null if there is none) that satisfies the VM and the goal.</returns>
public List <BinaryOption> MinimizeConfig(List <BinaryOption> config, VariabilityModel vm, bool minimize, List <BinaryOption> unWantedOptions)
{
List <BoolExpr> variables;
Dictionary <BoolExpr, BinaryOption> termToOption;
Dictionary <BinaryOption, BoolExpr> optionToTerm;
Tuple <Context, BoolExpr> z3Tuple = Z3Solver.GetInitializedBooleanSolverSystem(out variables, out optionToTerm, out termToOption, vm, this.henard);
Context z3Context = z3Tuple.Item1;
BoolExpr z3Constraints = z3Tuple.Item2;
List <BoolExpr> constraints = new List <BoolExpr>();
constraints.Add(z3Constraints);
//Feature Selection
foreach (BinaryOption binOpt in config)
{
BoolExpr term = optionToTerm[binOpt];
constraints.Add(term);
}
Model model = null;
if (minimize == true)
{
//Defining Goals
ArithExpr[] optimizationGoals = new ArithExpr[variables.Count];
for (int r = 0; r < variables.Count; r++)
{
BinaryOption currOption = termToOption[variables[r]];
ArithExpr numericVariable = z3Context.MkIntConst(currOption.Name);
int weight = 1;
if (unWantedOptions != null && (unWantedOptions.Contains(termToOption[variables[r]]) && !config.Contains(termToOption[variables[r]])))
{
weight = 1000;
}
constraints.Add(z3Context.MkEq(numericVariable, z3Context.MkITE(variables[r], z3Context.MkInt(weight), z3Context.MkInt(0))));
optimizationGoals[r] = numericVariable;
}
// For minimization, we need the class 'Optimize'
Optimize optimizer = z3Context.MkOptimize();
optimizer.Assert(constraints.ToArray());
optimizer.MkMinimize(z3Context.MkAdd(optimizationGoals));
if (optimizer.Check() != Status.SATISFIABLE)
{
return(new List <BinaryOption>());
}
else
{
model = optimizer.Model;
}
}
else
{
// Return the first configuration returned by the solver
Microsoft.Z3.Solver 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(constraints.ToArray());
if (solver.Check() != Status.SATISFIABLE)
{
return(new List <BinaryOption>());
}
else
{
model = solver.Model;
}
}
List <BinaryOption> result = RetrieveConfiguration(variables, model, termToOption);
return(result);
}
/// <summary>
/// Generates up to n solutions of the given variability model.
/// Note that this method could also generate less than n solutions if the variability model does not contain sufficient solutions.
/// Moreover, in the case that <code>n < 0</code>, all solutions are generated.
/// </summary>
/// <param name="vm">The <see cref="VariabilityModel"/> to obtain solutions for.</param>
/// <param name="n">The number of solutions to obtain.</param>
/// <returns>A list of configurations, in which a configuration is a list of SELECTED binary options.</returns>
public List <List <BinaryOption> > GenerateUpToNFast(VariabilityModel vm, int n)
{
// Use the random seed to produce new random seeds
Random random = new Random(Convert.ToInt32(z3RandomSeed));
List <BoolExpr> variables;
Dictionary <BoolExpr, BinaryOption> termToOption;
Dictionary <BinaryOption, BoolExpr> optionToTerm;
Tuple <Context, BoolExpr> z3Tuple = Z3Solver.GetInitializedBooleanSolverSystem(out variables, out optionToTerm, out termToOption, vm, this.henard, random.Next());
Context z3Context = z3Tuple.Item1;
BoolExpr z3Constraints = z3Tuple.Item2;
List <BoolExpr> excludedConfigurations = new List <BoolExpr>();
List <BoolExpr> constraints = Z3Solver.lastConstraints;
List <List <BinaryOption> > configurations = new List <List <BinaryOption> >();
Microsoft.Z3.Solver s = z3Context.MkSolver();
// TODO: The following line works for z3Solver version >= 4.6.0
//solver.Set (RANDOM_SEED, z3RandomSeed);
Params solverParameter = z3Context.MkParams();
if (henard)
{
solverParameter.Add(RANDOM_SEED, NextUInt(random));
}
else
{
solverParameter.Add(RANDOM_SEED, z3RandomSeed);
}
s.Parameters = solverParameter;
s.Assert(z3Constraints);
s.Push();
Model model = null;
while (s.Check() == Status.SATISFIABLE && (configurations.Count < n || n < 0))
{
model = s.Model;
List <BinaryOption> config = RetrieveConfiguration(variables, model, termToOption);
configurations.Add(config);
if (henard)
{
BoolExpr newConstraint = Z3Solver.NegateExpr(z3Context, Z3Solver.ConvertConfiguration(z3Context, config, optionToTerm, vm));
excludedConfigurations.Add(newConstraint);
Dictionary <BoolExpr, BinaryOption> oldTermToOption = termToOption;
// Now, initialize a new one for the next configuration
z3Tuple = Z3Solver.GetInitializedBooleanSolverSystem(out variables, out optionToTerm, out termToOption, vm, this.henard, random.Next());
z3Context = z3Tuple.Item1;
z3Constraints = z3Tuple.Item2;
s = z3Context.MkSolver();
//s.Set (RANDOM_SEED, NextUInt (random));
solverParameter = z3Context.MkParams();
solverParameter.Add(RANDOM_SEED, NextUInt(random));
s.Parameters = solverParameter;
constraints = Z3Solver.lastConstraints;
excludedConfigurations = Z3Solver.ConvertConstraintsToNewContext(oldTermToOption, optionToTerm, excludedConfigurations, z3Context);
constraints.AddRange(excludedConfigurations);
s.Assert(z3Context.MkAnd(Z3Solver.Shuffle(constraints, new Random(random.Next()))));
s.Push();
}
else
{
s.Add(Z3Solver.NegateExpr(z3Context, Z3Solver.ConvertConfiguration(z3Context, config, optionToTerm, vm)));
}
}
return(configurations);
}
/*!
\brief Extract unsatisfiable core example
*/
public void unsat_core_and_proof_example()
{
if (this.z3 != null)
{
this.z3.Dispose();
}
Dictionary<string, string> cfg = new Dictionary<string, string>() {
{ "PROOF_MODE", "2" } };
this.z3 = new Context(cfg);
this.solver = z3.MkSolver();
BoolExpr pa = mk_bool_var("PredA");
BoolExpr pb = mk_bool_var("PredB");
BoolExpr pc = mk_bool_var("PredC");
BoolExpr pd = mk_bool_var("PredD");
BoolExpr p1 = mk_bool_var("P1");
BoolExpr p2 = mk_bool_var("P2");
BoolExpr p3 = mk_bool_var("P3");
BoolExpr p4 = mk_bool_var("P4");
BoolExpr[] assumptions = new BoolExpr[] { z3.MkNot(p1), z3.MkNot(p2), z3.MkNot(p3), z3.MkNot(p4) };
BoolExpr f1 = z3.MkAnd(new BoolExpr[] { pa, pb, pc });
BoolExpr f2 = z3.MkAnd(new BoolExpr[] { pa, z3.MkNot(pb), pc });
BoolExpr f3 = z3.MkOr(z3.MkNot(pa), z3.MkNot(pc));
BoolExpr f4 = pd;
solver.Assert(z3.MkOr(f1, p1));
solver.Assert(z3.MkOr(f2, p2));
solver.Assert(z3.MkOr(f3, p3));
solver.Assert(z3.MkOr(f4, p4));
Status result = solver.Check(assumptions);
if (result == Status.UNSATISFIABLE)
{
Console.WriteLine("unsat");
Console.WriteLine("proof: {0}", solver.Proof);
foreach (Expr c in solver.UnsatCore)
{
Console.WriteLine("{0}", c);
}
}
}
private bool IsValid()
{
Microsoft.Z3.Status status = _solver.Check();
return(status == Status.SATISFIABLE);
}