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add tests & update docs/options.md
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‎core/src/test/kotlin/org/evomaster/core/search/algorithms/LIPSAlgorithmTest.kt‎

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@@ -12,6 +12,7 @@ import org.evomaster.core.search.algorithms.onemax.OneMaxIndividual
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import org.evomaster.core.search.algorithms.onemax.OneMaxModule
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import org.evomaster.core.search.algorithms.onemax.OneMaxSampler
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import org.evomaster.core.search.service.ExecutionPhaseController
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import org.evomaster.core.search.algorithms.observer.GARecorder
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import org.junit.jupiter.api.Assertions.assertEquals
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import org.junit.jupiter.api.Assertions.assertTrue
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import org.junit.jupiter.api.BeforeEach
@@ -53,6 +54,122 @@ class LIPSAlgorithmTest {
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)
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}
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// Edge Case: CrossoverProbability=0 on LIPS
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@Test
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fun testNoCrossoverWhenProbabilityZero_LIPS() {
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TestUtils.handleFlaky {
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val ga = injector.getInstance(
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Key.get(object : TypeLiteral<LIPSAlgorithm<OneMaxIndividual>>() {})
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)
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val rec = GARecorder<OneMaxIndividual>()
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ga.addObserver(rec)
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val config = injector.getInstance(EMConfig::class.java)
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config.gaSolutionSource = EMConfig.GASolutionSource.POPULATION
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config.maxEvaluations = 100_000
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config.stoppingCriterion = EMConfig.StoppingCriterion.ACTION_EVALUATIONS
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config.populationSize = 5
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config.xoverProbability = 0.0 // no crossover
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config.fixedRateMutation = 1.0 // force mutation
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ga.setupBeforeSearch()
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ga.searchOnce()
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val nextPop = ga.getViewOfPopulation()
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assertEquals(config.populationSize, nextPop.size)
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assertEquals(0, rec.xoCalls.size)
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// at least the offspring should have been mutated
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assertTrue(rec.mutated.size >= 1)
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}
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}
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// Edge Case: MutationProbability=0 on LIPS
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@Test
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fun testNoMutationWhenProbabilityZero_LIPS() {
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TestUtils.handleFlaky {
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val ga = injector.getInstance(
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Key.get(object : TypeLiteral<LIPSAlgorithm<OneMaxIndividual>>() {})
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)
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val rec = GARecorder<OneMaxIndividual>()
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ga.addObserver(rec)
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val config = injector.getInstance(EMConfig::class.java)
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config.gaSolutionSource = EMConfig.GASolutionSource.POPULATION
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config.maxEvaluations = 100_000
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config.stoppingCriterion = EMConfig.StoppingCriterion.ACTION_EVALUATIONS
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config.populationSize = 5
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config.xoverProbability = 1.0 // allow crossover
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config.fixedRateMutation = 0.0 // disable mutation
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ga.setupBeforeSearch()
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ga.searchOnce()
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val nextPop = ga.getViewOfPopulation()
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assertEquals(config.populationSize, nextPop.size)
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assertEquals(0, rec.mutated.size)
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}
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}
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// Next generation is elites + best from offspring
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@Test
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fun testNextGenerationIsElitesPlusBestFromOffspring_LIPS() {
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TestUtils.handleFlaky {
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val ga = injector.getInstance(
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Key.get(object : TypeLiteral<LIPSAlgorithm<OneMaxIndividual>>() {})
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)
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val rec = GARecorder<OneMaxIndividual>()
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ga.addObserver(rec)
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val config = injector.getInstance(EMConfig::class.java)
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config.populationSize = 5
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config.elitesCount = 1
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config.xoverProbability = 0.0
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config.fixedRateMutation = 1.0
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config.gaSolutionSource = EMConfig.GASolutionSource.POPULATION
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config.maxEvaluations = 100_000
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config.stoppingCriterion = EMConfig.StoppingCriterion.ACTION_EVALUATIONS
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ga.setupBeforeSearch()
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val initPop = ga.getViewOfPopulation()
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val eliteCount = config.elitesCount
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val mu = config.populationSize
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// Determine expected elites from current population
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val eliteScores = initPop
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.map { ga.score(it) }
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.sortedDescending()
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.take(eliteCount)
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ga.searchOnce()
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val finalPop = ga.getViewOfPopulation()
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// population size remains the same
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assertEquals(mu, finalPop.size)
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// offspring considered as those mutated
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val offspringScores = rec.mutated
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.map { ga.score(it) }
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.sortedDescending()
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val neededFromOffspring = mu - eliteCount
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val expectedScores = (eliteScores + offspringScores.take(neededFromOffspring))
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.sortedDescending()
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val finalScores = finalPop
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.map { ga.score(it) }
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.sortedDescending()
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assertEquals(expectedScores, finalScores)
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// All non-elite positions were filled by mutated offspring
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assertEquals(neededFromOffspring, rec.mutated.size)
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}
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}
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}
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‎docs/options.md‎

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@@ -68,7 +68,7 @@ There are 3 types of options:
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|`addPreDefinedTests`| __Boolean__. Add predefined tests at the end of the search. An example is a test to fetch the schema of RESTful APIs. *Default value*: `true`.|
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|`addTestComments`| __Boolean__. Add summary comments on each test. *Default value*: `true`.|
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|`advancedBlackBoxCoverage`| __Boolean__. Apply more advanced coverage criteria for black-box testing. This can result in larger generated test suites. *Default value*: `true`.|
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|`algorithm`| __Enum__. The algorithm used to generate test cases. The default depends on whether black-box or white-box testing is done. *Valid values*: `DEFAULT, SMARTS, MIO, RANDOM, WTS, MOSA, RW, StandardGA, MonotonicGA, SteadyStateGA`. *Default value*: `DEFAULT`.|
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|`algorithm`| __Enum__. The algorithm used to generate test cases. The default depends on whether black-box or white-box testing is done. *Valid values*: `DEFAULT, SMARTS, MIO, RANDOM, WTS, MOSA, RW, StandardGA, MonotonicGA, SteadyStateGA, LIPS`. *Default value*: `DEFAULT`.|
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|`allowInvalidData`| __Boolean__. When generating data, allow in some cases to use invalid values on purpose. *Default value*: `true`.|
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|`appendToStatisticsFile`| __Boolean__. Whether should add to an existing statistics file, instead of replacing it. *Default value*: `false`.|
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|`archiveAfterMutationFile`| __String__. Specify a path to save archive after each mutation during search, only useful for debugging. *DEBUG option*. *Default value*: `archive.csv`.|

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