How to use Exhaustive.Companion.ints method of io.kotest.property.exhaustive.ints class

Best Kotest code snippet using io.kotest.property.exhaustive.ints.Exhaustive.Companion.ints

Gen.kt

Source:Gen.kt

1package io.kotest.property2import io.kotest.property.arbitrary.of3/**4 * A [Gen] is responsible for providing values to be used in property testing. You can think of it as like5 * an input stream for values. Each arg will provide data for a specific type <A>.6 *7 * Gens can be created in two ways: with arbitrary (random) values from instances of [Arb] and8 * exhaustive values over a closed space from instances of [Exhaustive].9 *10 * Arbs generate random values across a given space. The values may be repeated, and some11 * values may never be generated at all. For example generating 1000 random integers between 0 and Int.MAX12 * will clearly not return all possible values, and some values may happen to be generated more than once.13 *14 * Exhaustives generate all values from a given space. This is useful when you want to ensure every15 * value in that space is used. For instance for enum values, it is usually more helpful to ensure each16 * enum is used, rather than picking randomly from the enums values.17 *18 * Both types of gens can be mixed and matched in property tests. For example,19 * you could test a function with 100 random positive integers (arbitrary) alongside every20 * even number from 0 to 200 (exhaustive).21 */22sealed class Gen<out A> {23   /**24    * Returns values from this generator as a lazily generated sequence.25    *26    * If this gen is an [Arb], then each value will either be a sample or an edge case. The bias27    * towards edge cases or samples is given by the value of [EdgeConfig.edgecasesGenerationProbability]28    * inside the [edgeConfig] parameter.29    *30    * If this gen is an [Exhaustive], then the returned values will iterate in turn, repeating31    * once exhausted as required.32    *33    */34   fun generate(35      rs: RandomSource,36      edgeConfig: EdgeConfig = EdgeConfig.default()37   ): Sequence<Sample<A>> =38      when (this) {39         is Arb -> {40            val samples = this.samples(rs).iterator()41            generateSequence {42               val isEdgeCase = rs.random.nextDouble(0.0, 1.0) < edgeConfig.edgecasesGenerationProbability43               if (isEdgeCase) {44                  this.edgecase(rs)?.asSample() ?: samples.next()45               } else samples.next()46            }47         }48         is Exhaustive -> {49            check(this.values.isNotEmpty()) { "Exhaustive.values shouldn't be a empty list." }50            generateSequence { this.values.map { Sample(it) } }.flatten()51         }52      }53   /**54    * Returns an optional [Classifier] to label values.55    */56   open val classifier: Classifier<out A>? = null57   /**58    * The minimum iteration count required for this [Gen] to be invoked.59    * Requesting a property test with fewer than this will result in an exception.60    */61   fun minIterations(): Int = when (this) {62      is Exhaustive -> this.values.size63      else -> 164   }65}66/**67 * An [Arb] (short for arbitrary) is a generator of values in two categories: edge cases and samples.68 *69 * Edge cases are values that are a common source of bugs. For example, a function using ints is70 * more likely to fail for common edge cases like zero, minus 1, positive 1, [Int.MAX_VALUE] and [Int.MIN_VALUE]71 * rather than random values like 965489. Therefore, it is useful that we try to include such values72 * rather than relying entirely on random values which are unlikely to generate these.73 *74 * Not all arbitraries will utilize edge cases. For example, if you define an integer generator75 * using a subset of the number space - say from 100 to 200 - then no edge cases would be provided.76 *77 * Samples are chosen randomly from the sample space and are used to give a greater breadth to78 * the test cases. For example, in the case of a function using integers, these random values79 * could be from across the entire integer number line, or could be limited to a subset of ints80 * such as natural numbers or even numbers.81 *82 * In addition to values, arbs can optionally implement a [classify] function which classifies83 * the generated values with labels. These labels can then be used to display information on the84 * types of values generated.85 *86 * In order to use an [Arb] outside a property test, one must invoke the [take] method, passing in87 * the number of iterations required and optionally a [ShrinkingMode].88 */89abstract class Arb<out A> : Gen<A>() {90   /**91    * Returns a single edge case for this arbitrary. If this arb supports multiple edge cases,92    * then one should be chosen randomly each time this function is invoked.93    *94    * Can return null if this arb does not provide edge cases.95    */96   abstract fun edgecase(rs: RandomSource): A?97   /**98    * Returns a single random [Sample] from this [Arb] using the supplied random source.99    */100   abstract fun sample(rs: RandomSource): Sample<A>101   /**102    * Returns a sequence from values generated from this arb.103    * Edgecases will be ignored.104    */105   fun samples(rs: RandomSource = RandomSource.default()): Sequence<Sample<A>> {106      return generateSequence { sample(rs) }107   }108   companion object109}110/**111 * An exhaustive is a type of [Gen] which generates an exhaustive set of values from a defined range.112 *113 * An example of a exhaustive is the sequence of integers from 0 to 100.114 * Another example is all strings of two characters.115 *116 * A progression is useful when you want to generate an exhaustive set of values from a given117 * sample space, rather than random values from that space. For example, if you were testing a118 * function that used an enum, you might prefer to guarantee that every enum value is used, rather119 * than selecting randomly from amongst the enum values (with possible duplicates and gaps).120 *121 * Exhaustives do not shrink their values. There is no need to find a smaller failing case, because122 * the smaller values will themselves naturally be included in the tested values.123 *124 * An exhaustive is less suitable when you have a large sample space you need to select values from.125 */126abstract class Exhaustive<out A> : Gen<A>() {127   /**128    * Returns the values of this [Exhaustive].129    */130   abstract val values: List<A>131   /**132    * Converts this into an [Arb] where the generated values of the returned arb133    * are choosen randomly from the values provided by this exhausive.134    */135   fun toArb(): Arb<A> = Arb.of(values)136   companion object137}138fun interface Classifier<A> {139   fun classify(value: A): String?140}141/**142 * Contains a single generated value from a [Gen] and an [RTree] of lazily evaluated shrinks.143 */144data class Sample<out A>(val value: A, val shrinks: RTree<A> = RTree({ value }))145fun <A> A.asSample(): Sample<A> = Sample(this)146/**147 * Returns a [Sample] with shrinks by using the supplied [Shrinker] against the input value [a].148 */149fun <A> sampleOf(a: A, shrinker: Shrinker<A>) = Sample(a, shrinker.rtree(a))150data class EdgeConfig(151   val edgecasesGenerationProbability: Double = PropertyTesting.defaultEdgecasesGenerationProbability152) {153   companion object;154   init {155      check(edgecasesGenerationProbability in 0.0..1.0) {156         "provided edgecasesProbability $edgecasesGenerationProbability is not between 0.0 and 1.0"157      }158   }159}...

Exhaustive.kt

Source:Exhaustive.kt

1package utils2import io.kotest.property.Arb3import io.kotest.property.Exhaustive4import io.kotest.property.Gen5import io.kotest.property.RandomSource6import io.kotest.property.arbitrary.byte7import io.kotest.property.arbitrary.next8import io.kotest.property.exhaustive.exhaustive9import io.kotest.property.exhaustive.filter10import io.kotest.property.exhaustive.ints11import io.kotest.property.exhaustive.map12fun <N : Number> Exhaustive<N>.toInt() = map { it.toInt() }13fun <N : Number> Exhaustive<N>.toShort() = map { it.toShort() }14fun Exhaustive.Companion.shorts(min: Short = Short.MIN_VALUE, max: Short = Short.MAX_VALUE) =15    Exhaustive.ints(min..max).map { it.toShort() }16fun Exhaustive.Companion.ubytes(min: UByte = UByte.MIN_VALUE, max: UByte = UByte.MAX_VALUE): Exhaustive<UByte> =17    Exhaustive.ints(min.toInt()..max.toInt()).map { it.toUByte() }18fun Exhaustive.Companion.ushorts(min: UShort = UShort.MIN_VALUE, max: UShort = UShort.MAX_VALUE): Exhaustive<UShort>  =19    Exhaustive.ints(min.toInt()..max.toInt()).map { it.toUShort() }20fun Exhaustive.Companion.byteArrays(length: IntRange, byte: Gen<Byte> = Arb.byte()): Exhaustive<ByteArray> {21    val generator = byte.generate(RandomSource.Default).iterator()22    return length.map { ByteArray(it) { generator.next().value } }.exhaustive()23}24operator fun <A> Exhaustive<A>.minus(other: Exhaustive<A>) =25    filter { it !in other.values }26inline fun <reified T> Exhaustive.Companion.arrayOf(value: Arb<T>, length: IntRange): Exhaustive<Array<T>> {27    return length.map { Array(it) { value.next() } }.exhaustive()28}...

ints.kt

Source:ints.kt

1package io.kotest.property.exhaustive2import io.kotest.property.Exhaustive3fun Exhaustive.Companion.ints(range: IntRange): Exhaustive<Int> = range.toList().exhaustive()4fun Exhaustive.Companion.longs(range: LongRange): Exhaustive<Long> = range.toList().exhaustive()...

Exhaustive.Companion.ints

Using AI Code Generation

1val intList = io.kotest.property.exhaustive.ints(1, 10).take(10)2val intList = io.kotest.property.exhaustive.ints(1, 10).take(10)3val intList = io.kotest.property.exhaustive.ints(1, 10).take(10)4val intList = io.kotest.property.exhaustive.ints(1, 10).take(10)5val intList = io.kotest.property.exhaustive.ints(1, 10).take(10)6val intList = io.kotest.property.exhaustive.ints(1, 10).take(10)7val intList = io.kotest.property.exhaustive.ints(1, 10).take(10)8val intList = io.kotest.property.exhaustive.ints(1, 10).take(10)9val intList = io.kotest.property.exhaustive.ints(1, 10).take(10)10val intList = io.kotest.property.exhaustive.ints(1, 10).take(10)11val intList = io.kotest.property.exhaustive.ints(1, 10).take(10)

Exhaustive.Companion.ints

Using AI Code Generation

1val ints = Exhaustive.ints(1, 3)2ints.iterator().forEach { println(it) }3val ints = Exhaustive.ints(1, 3)4ints.iterator().forEach { println(it) }5val ints = Exhaustive.ints(1, 3)6ints.iterator().forEach { println(it) }7val ints = Exhaustive.ints(1, 3)8ints.iterator().forEach { println(it) }9val ints = Exhaustive.ints(1, 3)10ints.iterator().forEach { println(it) }11val ints = Exhaustive.ints(1, 3)12ints.iterator().forEach { println(it) }13val ints = Exhaustive.ints(1, 3)14ints.iterator().forEach { println(it) }15val ints = Exhaustive.ints(1, 3)16ints.iterator().forEach { println(it) }17val ints = Exhaustive.ints(1, 3)18ints.iterator().forEach { println(it) }19val ints = Exhaustive.ints(1, 3

Exhaustive.Companion.ints

Using AI Code Generation

1    val ints = Exhaustive.ints(1..10)2    val ints = Exhaustive.ints(1, 2, 3, 4, 5)3    val ints = Exhaustive.ints(1..10, 11..20)4    val ints = Exhaustive.ints(1..10, 11..20, 21..30)5    val ints = Exhaustive.ints(1..10, 11..20, 21..30, 31..40)6    val ints = Exhaustive.ints(1..10, 11..20, 21..30, 31..40, 41..50)7    val ints = Exhaustive.ints(1..10, 11..20, 21..30, 31..40, 41..50, 51..60)8    val ints = Exhaustive.ints(1..10, 11..20, 21..30, 31..40, 41..50, 51..60, 61..70)

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