org.evosuite.strategy.PropertiesSuiteGAFactory Maven / Gradle / Ivy
/**
* Copyright (C) 2010-2018 Gordon Fraser, Andrea Arcuri and EvoSuite
* contributors
*
* This file is part of EvoSuite.
*
* EvoSuite is free software: you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as published
* by the Free Software Foundation, either version 3.0 of the License, or
* (at your option) any later version.
*
* EvoSuite is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with EvoSuite. If not, see .
*/
package org.evosuite.strategy;
import org.evosuite.Properties;
import org.evosuite.Properties.Criterion;
import org.evosuite.Properties.Strategy;
import org.evosuite.Properties.TheReplacementFunction;
import org.evosuite.ShutdownTestWriter;
import org.evosuite.TestGenerationContext;
import org.evosuite.coverage.branch.BranchPool;
import org.evosuite.coverage.mutation.MutationTestPool;
import org.evosuite.coverage.mutation.MutationTimeoutStoppingCondition;
import org.evosuite.ga.ChromosomeFactory;
import org.evosuite.ga.FitnessReplacementFunction;
import org.evosuite.ga.archive.ArchiveTestChromosomeFactory;
import org.evosuite.ga.metaheuristics.*;
import org.evosuite.ga.metaheuristics.mosa.MOSA;
import org.evosuite.ga.metaheuristics.mosa.DynaMOSA;
import org.evosuite.ga.metaheuristics.mulambda.MuLambdaEA;
import org.evosuite.ga.metaheuristics.mulambda.MuPlusLambdaEA;
import org.evosuite.ga.metaheuristics.mulambda.OnePlusLambdaLambdaGA;
import org.evosuite.ga.metaheuristics.mulambda.OnePlusOneEA;
import org.evosuite.ga.operators.crossover.CrossOverFunction;
import org.evosuite.ga.operators.crossover.SinglePointCrossOver;
import org.evosuite.ga.operators.crossover.SinglePointFixedCrossOver;
import org.evosuite.ga.operators.crossover.SinglePointRelativeCrossOver;
import org.evosuite.ga.operators.crossover.UniformCrossOver;
import org.evosuite.ga.operators.ranking.FastNonDominatedSorting;
import org.evosuite.ga.operators.ranking.RankBasedPreferenceSorting;
import org.evosuite.ga.operators.ranking.RankingFunction;
import org.evosuite.ga.operators.selection.BestKSelection;
import org.evosuite.ga.operators.selection.BinaryTournamentSelectionCrowdedComparison;
import org.evosuite.ga.operators.selection.FitnessProportionateSelection;
import org.evosuite.ga.operators.selection.RankSelection;
import org.evosuite.ga.operators.selection.RandomKSelection;
import org.evosuite.ga.operators.selection.SelectionFunction;
import org.evosuite.ga.operators.selection.TournamentSelection;
import org.evosuite.ga.operators.selection.TournamentSelectionRankAndCrowdingDistanceComparator;
import org.evosuite.ga.stoppingconditions.GlobalTimeStoppingCondition;
import org.evosuite.ga.stoppingconditions.MaxTimeStoppingCondition;
import org.evosuite.ga.stoppingconditions.RMIStoppingCondition;
import org.evosuite.ga.stoppingconditions.SocketStoppingCondition;
import org.evosuite.ga.stoppingconditions.StoppingCondition;
import org.evosuite.ga.stoppingconditions.ZeroFitnessStoppingCondition;
import org.evosuite.regression.RegressionTestSuiteChromosomeFactory;
import org.evosuite.statistics.StatisticsListener;
import org.evosuite.testcase.factories.AllMethodsTestChromosomeFactory;
import org.evosuite.testcase.factories.JUnitTestCarvedChromosomeFactory;
import org.evosuite.testcase.factories.RandomLengthTestFactory;
import org.evosuite.testcase.localsearch.BranchCoverageMap;
import org.evosuite.testsuite.RelativeSuiteLengthBloatControl;
import org.evosuite.testsuite.TestSuiteChromosome;
import org.evosuite.testsuite.TestSuiteReplacementFunction;
import org.evosuite.testsuite.factories.SerializationSuiteChromosomeFactory;
import org.evosuite.testsuite.factories.TestSuiteChromosomeFactory;
import org.evosuite.testsuite.secondaryobjectives.TestSuiteSecondaryObjective;
import org.evosuite.utils.ArrayUtil;
import org.evosuite.utils.ResourceController;
import sun.misc.Signal;
/**
* Factory for GA on test suites
*
* @author gordon
*
*/
@SuppressWarnings("restriction")
public class PropertiesSuiteGAFactory extends PropertiesSearchAlgorithmFactory {
protected ChromosomeFactory getChromosomeFactory() {
switch (Properties.STRATEGY) {
case EVOSUITE:
switch (Properties.TEST_FACTORY) {
case ALLMETHODS:
logger.info("Using all methods chromosome factory");
return new TestSuiteChromosomeFactory(
new AllMethodsTestChromosomeFactory());
case RANDOM:
logger.info("Using random chromosome factory");
return new TestSuiteChromosomeFactory(new RandomLengthTestFactory());
case ARCHIVE:
logger.info("Using archive chromosome factory");
return new TestSuiteChromosomeFactory(new ArchiveTestChromosomeFactory());
case JUNIT:
logger.info("Using seeding chromosome factory");
JUnitTestCarvedChromosomeFactory factory = new JUnitTestCarvedChromosomeFactory(
new RandomLengthTestFactory());
return new TestSuiteChromosomeFactory(factory);
case SERIALIZATION:
logger.info("Using serialization seeding chromosome factory");
return new SerializationSuiteChromosomeFactory(
new RandomLengthTestFactory());
default:
throw new RuntimeException("Unsupported test factory: "
+ Properties.TEST_FACTORY);
}
case REGRESSION:
return new RegressionTestSuiteChromosomeFactory();
case MOSUITE:
return new TestSuiteChromosomeFactory(new RandomLengthTestFactory());
default:
throw new RuntimeException("Unsupported test factory: "
+ Properties.TEST_FACTORY);
}
}
protected GeneticAlgorithm getGeneticAlgorithm(ChromosomeFactory factory) {
switch (Properties.ALGORITHM) {
case ONE_PLUS_ONE_EA:
logger.info("Chosen search algorithm: (1+1)EA");
{
OnePlusOneEA ga = new OnePlusOneEA(factory);
return ga;
}
case MU_PLUS_LAMBDA_EA:
logger.info("Chosen search algorithm: (Mu+Lambda)EA");
{
MuPlusLambdaEA ga = new MuPlusLambdaEA(factory, Properties.MU, Properties.LAMBDA);
return ga;
}
case MU_LAMBDA_EA:
logger.info("Chosen search algorithm: (Mu,Lambda)EA");
return new MuLambdaEA(factory, Properties.MU, Properties.LAMBDA);
case MONOTONIC_GA:
logger.info("Chosen search algorithm: MonotonicGA");
{
MonotonicGA ga = new MonotonicGA(factory);
if (Properties.REPLACEMENT_FUNCTION == TheReplacementFunction.FITNESSREPLACEMENT) {
// user has explicitly asked for this replacement function
ga.setReplacementFunction(new FitnessReplacementFunction());
} else {
// use default
ga.setReplacementFunction(new TestSuiteReplacementFunction());
}
return ga;
}
case CELLULAR_GA:
logger.info("Chosen search algorithm: CellularGA");
{
CellularGA ga = new CellularGA(Properties.MODEL, factory);
if (Properties.REPLACEMENT_FUNCTION == TheReplacementFunction.FITNESSREPLACEMENT) {
// user has explicitly asked for this replacement function
ga.setReplacementFunction(new FitnessReplacementFunction());
} else {
// use default
ga.setReplacementFunction(new TestSuiteReplacementFunction());
}
return ga;
}
case STEADY_STATE_GA:
logger.info("Chosen search algorithm: Steady-StateGA");
{
SteadyStateGA ga = new SteadyStateGA<>(factory);
if (Properties.REPLACEMENT_FUNCTION == TheReplacementFunction.FITNESSREPLACEMENT) {
// user has explicitly asked for this replacement function
ga.setReplacementFunction(new FitnessReplacementFunction());
} else {
// use default
ga.setReplacementFunction(new TestSuiteReplacementFunction());
}
return ga;
}
case BREEDER_GA:
logger.info("Chosen search algorithm: BreederGA");
{
BreederGA ga = new BreederGA<>(factory);
return ga;
}
case RANDOM_SEARCH:
logger.info("Chosen search algorithm: Random");
{
RandomSearch ga = new RandomSearch(factory);
return ga;
}
case NSGAII:
logger.info("Chosen search algorithm: NSGAII");
return new NSGAII(factory);
case SPEA2:
logger.info("Chosen search algorithm: SPEA2");
return new SPEA2(factory);
case MOSA:
logger.info("Chosen search algorithm: MOSA");
return new MOSA(factory);
case DYNAMOSA:
logger.info("Chosen search algorithm: DynaMOSA");
return new DynaMOSA(factory);
case ONE_PLUS_LAMBDA_LAMBDA_GA:
logger.info("Chosen search algorithm: 1 + (lambda, lambda)GA");
{
OnePlusLambdaLambdaGA ga = new OnePlusLambdaLambdaGA(factory, Properties.LAMBDA);
return ga;
}
case MIO:
logger.info("Chosen search algorithm: MIO");
{
MIO ga = new MIO(factory);
return ga;
}
case STANDARD_CHEMICAL_REACTION:
logger.info("Chosen search algorithm: Standard Chemical Reaction Optimization");
{
StandardChemicalReaction ga = new StandardChemicalReaction(factory);
return ga;
}
case LIPS:
logger.info("Chosen search algorithm: LIPS");
return new LIPS(factory);
default:
logger.info("Chosen search algorithm: StandardGA");
{
StandardGA ga = new StandardGA(factory);
return ga;
}
}
}
protected SelectionFunction getSelectionFunction() {
switch (Properties.SELECTION_FUNCTION) {
case ROULETTEWHEEL:
return new FitnessProportionateSelection<>();
case TOURNAMENT:
return new TournamentSelection<>();
case BINARY_TOURNAMENT:
return new BinaryTournamentSelectionCrowdedComparison<>();
case RANK_CROWD_DISTANCE_TOURNAMENT:
return new TournamentSelectionRankAndCrowdingDistanceComparator<>();
case BESTK:
return new BestKSelection<>();
case RANDOMK:
return new RandomKSelection<>();
default:
return new RankSelection<>();
}
}
protected CrossOverFunction getCrossoverFunction() {
switch (Properties.CROSSOVER_FUNCTION) {
case SINGLEPOINTFIXED:
return new SinglePointFixedCrossOver();
case SINGLEPOINTRELATIVE:
return new SinglePointRelativeCrossOver();
case SINGLEPOINT:
return new SinglePointCrossOver();
case COVERAGE:
if (Properties.STRATEGY != Properties.Strategy.EVOSUITE)
throw new RuntimeException(
"Coverage crossover function requires test suite mode");
return new org.evosuite.ga.operators.crossover.CoverageCrossOver();
case UNIFORM:
return new UniformCrossOver();
default:
throw new RuntimeException("Unknown crossover function: "
+ Properties.CROSSOVER_FUNCTION);
}
}
private RankingFunction getRankingFunction() {
switch (Properties.RANKING_TYPE) {
case FAST_NON_DOMINATED_SORTING:
return new FastNonDominatedSorting<>();
case PREFERENCE_SORTING:
default:
return new RankBasedPreferenceSorting<>();
}
}
@Override
public GeneticAlgorithm getSearchAlgorithm() {
ChromosomeFactory factory = getChromosomeFactory();
// FIXXME
GeneticAlgorithm ga = getGeneticAlgorithm(factory);
if (Properties.NEW_STATISTICS)
ga.addListener(new StatisticsListener());
// How to select candidates for reproduction
SelectionFunction selectionFunction = getSelectionFunction();
selectionFunction.setMaximize(false);
ga.setSelectionFunction(selectionFunction);
RankingFunction ranking_function = getRankingFunction();
ga.setRankingFunction(ranking_function);
// When to stop the search
StoppingCondition stopping_condition = getStoppingCondition();
ga.setStoppingCondition(stopping_condition);
// ga.addListener(stopping_condition);
if (Properties.STOP_ZERO) {
ga.addStoppingCondition(new ZeroFitnessStoppingCondition());
}
if (!(stopping_condition instanceof MaxTimeStoppingCondition)) {
ga.addStoppingCondition(new GlobalTimeStoppingCondition());
}
if (ArrayUtil.contains(Properties.CRITERION, Criterion.MUTATION)
|| ArrayUtil.contains(Properties.CRITERION, Criterion.STRONGMUTATION)) {
if (Properties.STRATEGY == Strategy.ONEBRANCH)
ga.addStoppingCondition(new MutationTimeoutStoppingCondition());
else
ga.addListener(new MutationTestPool());
// } else if (Properties.CRITERION == Criterion.DEFUSE) {
// if (Properties.STRATEGY == Strategy.EVOSUITE)
// ga.addListener(new DefUseTestPool());
}
ga.resetStoppingConditions();
ga.setPopulationLimit(getPopulationLimit());
// How to cross over
CrossOverFunction crossover_function = getCrossoverFunction();
ga.setCrossOverFunction(crossover_function);
// What to do about bloat
// MaxLengthBloatControl bloat_control = new MaxLengthBloatControl();
// ga.setBloatControl(bloat_control);
if (Properties.CHECK_BEST_LENGTH) {
RelativeSuiteLengthBloatControl bloat_control = new org.evosuite.testsuite.RelativeSuiteLengthBloatControl();
ga.addBloatControl(bloat_control);
ga.addListener(bloat_control);
}
// ga.addBloatControl(new MaxLengthBloatControl());
TestSuiteSecondaryObjective.setSecondaryObjectives();
// Some statistics
//if (Properties.STRATEGY == Strategy.EVOSUITE)
// ga.addListener(SearchStatistics.getInstance());
// ga.addListener(new MemoryMonitor());
// ga.addListener(MutationStatistics.getInstance());
// ga.addListener(BestChromosomeTracker.getInstance());
if (Properties.DYNAMIC_LIMIT) {
// max_s = GAProperties.generations * getBranches().size();
// TODO: might want to make this dependent on the selected coverage
// criterion
// TODO also, question: is branchMap.size() really intended here?
// I think BranchPool.getBranchCount() was intended
Properties.SEARCH_BUDGET = Properties.SEARCH_BUDGET
* (BranchPool.getInstance(TestGenerationContext.getInstance().getClassLoaderForSUT()).getNumBranchlessMethods(Properties.TARGET_CLASS) + BranchPool.getInstance(TestGenerationContext.getInstance().getClassLoaderForSUT()).getBranchCountForClass(Properties.TARGET_CLASS) * 2);
stopping_condition.setLimit(Properties.SEARCH_BUDGET);
logger.info("Setting dynamic length limit to " + Properties.SEARCH_BUDGET);
}
if (Properties.LOCAL_SEARCH_RESTORE_COVERAGE) {
org.evosuite.ga.metaheuristics.SearchListener map = BranchCoverageMap.getInstance();
ga.addListener(map);
}
if (Properties.SHUTDOWN_HOOK) {
// ShutdownTestWriter writer = new
// ShutdownTestWriter(Thread.currentThread());
ShutdownTestWriter writer = new ShutdownTestWriter();
ga.addStoppingCondition(writer);
RMIStoppingCondition rmi = RMIStoppingCondition.getInstance();
ga.addStoppingCondition(rmi);
if (Properties.STOPPING_PORT != -1) {
SocketStoppingCondition ss = new SocketStoppingCondition();
ss.accept();
ga.addStoppingCondition(ss);
}
// Runtime.getRuntime().addShutdownHook(writer);
Signal.handle(new Signal("INT"), writer);
}
ga.addListener(new ResourceController());
return ga;
}
}