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  • package scalatest

    ScalaTest's main traits, classes, and other members, including members supporting ScalaTest's DSL for the Scala interpreter.

    ScalaTest's main traits, classes, and other members, including members supporting ScalaTest's DSL for the Scala interpreter.

    Definition Classes
    org
  • package prop

    Scalatest support for Property-based testing.

    Scalatest support for Property-based testing.

    Introduction to Property-based Testing

    In traditional unit testing, you write tests that describe precisely what the test will do: create these objects, wire them together, call these functions, assert on the results, and so on. It is clear and deterministic, but also limited, because it only covers the exact situations you think to test. In most cases, it is not feasible to test all of the possible combinations of data that might arise in real-world use.

    Property-based testing works the other way around. You describe properties -- rules that you expect your classes to live by -- and describe how to test those properties. The test system then generates relatively large amounts of synthetic data (with an emphasis on edge cases that tend to make things break), so that you can see if the properties hold true in these situations.

    As a result, property-based testing is scientific in the purest sense: you are stating a hypothesis about how things should work (the property), and the system is trying to falsify that hypothesis. If the tests pass, that doesn't prove the property holds, but it at least gives you some confidence that you are probably correct.

    Property-based testing is deliberately a bit random: while the edge cases get tried upfront, the system also usually generates a number of random values to try out. This makes things a bit non-deterministic -- each run will be tried with somewhat different data. To make it easier to debug, and to build regression tests, the system provides tools to re-run a failed test with precisely the same data.

    Background

    TODO: Bill should insert a brief section on QuickCheck, ScalaCheck, etc, and how this system is similar and different.

    Using Property Checks

    In order to use the tools described here, you should import this package:

    import org.scalatest._
import org.scalatest.prop._

    This library is designed to work well with the types defined in Scalactic, and some functions take types such as PosZInt as parameters. So it can also be helpful to import those with:

    import org.scalactic.anyvals._

    In order to call forAll, the function that actually performs property checks, you will need to either extend or import GeneratorDrivenPropertyChecks, like this:

    class DocExamples extends FlatSpec with Matchers with GeneratorDrivenPropertyChecks {

    There's nothing special about FlatSpec, though -- you may use any of ScalaTest's styles with property checks. GeneratorDrivenPropertyChecks extends CommonGenerators, so it also provides access to the many utilities found there.

    What Does a Property Look Like?

    Let's check a simple property of Strings -- that if you concatenate a String to itself, its length will be doubled:

    "Strings" should "have the correct length when doubled" in {
  forAll { (s: String) =>
    val s2 = s * 2
    s2.length should equal (s.length * 2)
  }
}

    (Note that the examples here are all using the FlatSpec style, but will work the same way with any of ScalaTest's styles.)

    As the name of the tests suggests, the property we are testing is the length of a String that has been doubled.

    The test begins with forAll. This is usually the way you'll want to begin property checks, and that line can be read as, "For all Strings, the following should be true".

    The test harness will generate a number of Strings, with various contents and lengths. For each one, we compute s * 2. (* is a function on String, which appends the String to itself as many times as you specify.) And then we check that the length of the doubled String is twice the length of the original one.

    Using Specific Generators

    Let's try a more general version of this test, multiplying arbitrary Strings by arbitrary multipliers:

    "Strings" should "have the correct length when multiplied" in {
  forAll { (s: String, n: PosZInt) =>
    val s2 = s * n.value
    s2.length should equal (s.length * n.value)
  }
}

    Again, you can read the first line of the test as "For all Strings, and all non-negative Integers, the following should be true". (PosZInt is a type defined in Scalactic, which can be any positive integer, including zero. It is appropriate to use here, since multiplying a String by a negative number doesn't make sense.)

    This intuitively makes sense, but when we try to run it, we get a JVM Out of Memory error! Why? Because the test system tries to test with the "edge cases" first, and one of the more important edge cases is Int.MaxValue. It is trying to multiply a String by that, which is far larger than the memory of even a big computer, and crashing.

    So we want to constrain our test to sane values of n, so that it doesn't crash. We can do this by using more specific Generators.

    When we write a forAll test like the above, ScalaTest has to generate the values to be tested -- the semi-random Strings, Ints and other types that you are testing. It does this by calling on an implicit Generator for the desired type. The Generator generates values to test, starting with the edge cases and then moving on to randomly-selected values.

    ScalaTest has built-in Generators for many major types, including String and PosZInt, but these Generators are generic: they will try any value, including values that can break your test, as shown above. But it also provides tools to let you be more specific.

    Here is the fixed version of the above test:

    "Strings" should "have the correct length when multiplied" in {
  forAll(strings, posZIntsBetween(0, 1000))
  { (s: String, n: PosZInt) =>
    val s2 = s * n.value
    s2.length should equal (s.length * n.value)
  }
}

    This is using a variant of forAll, which lets you specify the Generators to use instead of just picking the implicit one. CommonGenerators.strings is the built-in Generator for Strings, the same one you were getting implicitly. (The other built-ins can be found in CommonGenerators. They are mixed into GeneratorDrivenPropertyChecks, so they are readily available.)

    But CommonGenerators.posZIntsBetween is a function that creates a Generator that selects from the given values. In this case, it will create a Generator that only creates numbers from 0 to 1000 -- small enough to not blow up our computer's memory. If you try this test, this runs correctly.

    The moral of the story is that, while using the built-in Generators is very convenient, and works most of the time, you should think about the data you are trying to test, and pick or create a more-specific Generator when the test calls for it.

    CommonGenerators contains many functions that are helpful in common cases. In particular:

    • xxsBetween (where xxs might be Int, Long, Float or most other significant numeric types) gives you a value of the desired type in the given range, as in the posZIntsBetween() example above.
    • CommonGenerators.specificValue and CommonGenerators.specificValues create Generators that produce either one specific value every time, or one of several values randomly. This is useful for enumerations and types that behave like enumerations.
    • CommonGenerators.evenly and CommonGenerators.frequency create higher-level Generators that call other Generators, either more or less equally or with a distribution you define.

    Testing Your Own Types

    Testing the built-in types isn't very interesting, though. Usually, you have your own types that you want to check the properties of. So let's build up an example piece by piece.

    Say you have this simple type:

    sealed trait Shape {
  def area: Double
}
case class Rectangle(width: Int, height: Int) extends Shape {
  require(width > 0)
  require(height > 0)
  def area: Double = width * height
}

    Let's confirm a nice straightforward property that is surely true: that the area is greater than zero:

    "Rectangles" should "have a positive area" in {
   forAll { (w: PosInt, h: PosInt) =>
     val rect = Rectangle(w, h)
     rect.area should be > 0.0
   }
 }

    Note that, even though our class takes ordinary Ints as parameters (and checks the values at runtime), it is actually easier to generate the legal values using Scalactic's PosInt type.

    This should work, right? Actually, it doesn't -- if we run it a few times, we quickly hit an error!

    [info] Rectangles
[info] - should have a positive area *** FAILED ***
[info]   GeneratorDrivenPropertyCheckFailedException was thrown during property evaluation.
[info]    (DocExamples.scala:42)
[info]     Falsified after 2 successful property evaluations.
[info]     Location: (DocExamples.scala:42)
[info]     Occurred when passed generated values (
[info]       None = PosInt(399455539),
[info]       None = PosInt(703518968)
[info]     )
[info]     Init Seed: 1568878346200

    TODO: fix the above error to reflect the better errors we should get when we merge in the code being forward-ported from 3.0.5.

    Looking at it, we can see that the numbers being used are pretty large. What happens when we multiply them together?

    scala> 399455539 * 703518968
res0: Int = -2046258840

    We're hitting an Int overflow problem here: the numbers are too big to multiply together and still get an Int. So we have to fix our area function:

    case class Rectangle(width: Int, height: Int) extends Shape {
  require(width > 0)
  require(height > 0)
  def area: Double = width.toLong * height.toLong
}

    Now, when we run our property check, it consistently passes. Excellent -- we've caught a bug, because ScalaTest tried sufficiently large numbers.

    Composing Your Own Generators

    Doing things as shown above works, but having to generate the parameters and construct a Rectangle every time is a nuisance. What we really want is to create our own Generator that just hands us Rectangles, the same way we can do for PosInt. Fortunately, this is easy.

    Generators can be composed in for comprehensions. So we can create our own Generator for Rectangle like this:

    implicit val rectGenerator = for {
  w <- posInts
  h <- posInts
}
  yield Rectangle(w, h)

    Taking that line by line:

    w <- posInts

    CommonGenerators.posInts is the built-in Generator for positive Ints. So this line puts a randomly-generated positive Int in w, and

    h <- posInts

    this line puts another one in h. Finally, this line:

    yield Rectangle(w, h)

    combines w and h to make a Rectangle.

    That's pretty much all you need in order to build any normal case class -- just build it out of the Generators for the type of each field. (And if the fields are complex data structures themselves, build Generators for them the same way, until you are just using primitives.)

    Now, our property check becomes simpler:

    "Generated Rectangles" should "have a positive area" in {
   forAll { (rect: Rectangle) =>
     rect.area should be > 0.0
   }
 }

    That's about as close to plain English as we can reasonably hope for!

    Filtering Values with whenever()

    Sometimes, not all of your generated values make sense for the property you want to check -- you know (via external information) that some of these values will never come up. In cases like this, you can create a custom Generator that only creates the values you do want, but it's often easier to just use Whenever.whenever. (Whenever is mixed into GeneratorDrivenPropertyChecks, so this is available when you need it.)

    The Whenever.whenever function can be used inside of GeneratorDrivenPropertyChecks.forAll. It says that only the filtered values should be used, and anything else should be discarded. For example, look at this property:

    "Fractions" should "get smaller when squared" in {
  forAll { (n: Float) =>
    whenever(n > 0 && n < 1) {
      (n * n) should be < n
    }
  }
}

    We are testing a property of numbers less than 1, so we filter away everything that is not the numbers we want. This property check succeeds, because we've screened out the values that would make it fail.

    Discard Limits

    You shouldn't push Whenever.whenever too far, though. This system is all about trying random data, but if too much of the random data simply isn't usable, you can't get valid answers, and the system tracks that.

    For example, consider this apparently-reasonable test:

    "Space Chars" should "not also be letters" in {
  forAll { (c: Char) =>
    whenever (c.isSpaceChar) {
      assert(!c.isLetter)
    }
  }
}

    Although the property is true, this test will fail with an error like this:

    [info] Lowercase Chars
[info] - should upper-case correctly *** FAILED ***
[info]   Gave up after 0 successful property evaluations. 49 evaluations were discarded.
[info]   Init Seed: 1568855247784

    Because the vast majority of Chars are not spaces, nearly all of the generated values are being discarded. As a result, the system gives up after a while. In cases like this, you usually should write a custom Generator instead.

    The proportion of how many discards to permit, relative to the number of successful checks, is configuration-controllable. See GeneratorDrivenPropertyChecks for more details.

    Randomization

    The point of Generator is to create pseudo-random values for checking properties. But it turns out to be very inconvenient if those values are actually random -- that would mean that, when a property check fails occasionally, you have no good way to invoke that specific set of circumstances again for debugging. We want "randomness", but we also want it to be deterministic, and reproducible when you need it.

    To support this, all "randomness" in ScalaTest's property checking system uses the Randomizer class. You start by creating a Randomizer using an initial seed value, and call that to get your "random" value. Each call to a Randomizer function returns a new Randomizer, which you should use to fetch the next value.

    GeneratorDrivenPropertyChecks.forAll uses Randomizer under the hood: each time you run a forAll-based test, it will automatically create a new Randomizer, which by default is seeded based on the current system time. You can override this, as discussed below.

    Since Randomizer is actually deterministic (the "random" values are unobvious, but will always be the same given the same initial seed), this means that re-running a test with the same seed will produce the same values.

    If you need random data for your own Generators and property checks, you should use Randomizer in the same way; that way, your tests will also be re-runnable, when needed for debugging.

    Debugging, and Re-running a Failed Property Check

    In Testing Your Own Types above, we found to our surprise that the property check failed with this error:

    [info] Rectangles
[info] - should have a positive area *** FAILED ***
[info]   GeneratorDrivenPropertyCheckFailedException was thrown during property evaluation.
[info]    (DocExamples.scala:42)
[info]     Falsified after 2 successful property evaluations.
[info]     Location: (DocExamples.scala:42)
[info]     Occurred when passed generated values (
[info]       None = PosInt(399455539),
[info]       None = PosInt(703518968)
[info]     )
[info]     Init Seed: 1568878346200

    There must be a bug here -- but once we've fixed it, how can we make sure that we are re-testing exactly the same case that failed?

    This is where the pseudo-random nature of Randomizer comes in, and why it is so important to use it consistently. So long as all of our "random" data comes from that, then all we need to do is re-run with the same seed.

    That's why the Init Seed shown in the message above is crucial. We can re-use that seed -- and therefore get exactly the same "random" data -- by using the -S flag to ScalaTest.

    So you can run this command in sbt to re-run exactly the same property check:

    testOnly *DocExamples -- -z "have a positive area" -S 1568878346200

    Taking that apart:

    • testOnly *DocExamples says that we only want to run suites whose paths end with DocExamples
    • -z "have a positive area" says to only run tests whose names include that string.
    • -S 1568878346200 says to run all tests with a "random" seed of 1568878346200

    By combining these flags, you can re-run exactly the property check you need, with the right random seed to make sure you are re-creating the failed test. You should get exactly the same failure over and over until you fix the bug, and then you can confirm your fix with confidence.

    Configuration

    In general, forAll() works well out of the box. But you can tune several configuration parameters when needed. See GeneratorDrivenPropertyChecks for info on how to set configuration parameters for your test.

    Table-Driven Properties

    Sometimes, you want something in between traditional hard-coded unit tests and Generator-driven, randomized tests. Instead, you sometimes want to check your properties against a specific set of inputs.

    (This is particularly useful for regression tests, when you have found certain inputs that have caused problems in the past, and want to make sure that they get consistently re-tested.)

    ScalaTest supports these, by mixing in TableDrivenPropertyChecks. See the documentation for that class for the full details.

    Definition Classes
    scalatest
  • Chooser
  • Classification
  • CommonGenerators
  • Configuration
  • Generator
  • GeneratorDrivenPropertyChecks
  • HavingLength
  • HavingSize
  • PrettyFunction0
  • PropertyArgument
  • PropertyCheckResult
  • PropertyChecks
  • Randomizer
  • SizeParam
  • TableDrivenPropertyChecks
  • TableFor1
  • TableFor10
  • TableFor11
  • TableFor12
  • TableFor13
  • TableFor14
  • TableFor15
  • TableFor16
  • TableFor17
  • TableFor18
  • TableFor19
  • TableFor2
  • TableFor20
  • TableFor21
  • TableFor22
  • TableFor3
  • TableFor4
  • TableFor5
  • TableFor6
  • TableFor7
  • TableFor8
  • TableFor9
  • Tables
  • Whenever

object Generator

Companion to the Generator trait, which contains many of the standard implicit Generators.

For the most part, you should not need to use the values and functions in here directly; the useful values in here are generally aliased in CommonGenerators (albeit with different names), which in turn is mixed into GeneratorDrivenPropertyChecks and TableDrivenPropertyChecks.

Note that this provides Generators for the common Scalactic types, as well as the common standard library ones.

Source
Generator.scala
Linear Supertypes
AnyRef, Any
Ordering
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Inherited
  1. Generator
  2. AnyRef
  3. Any
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Visibility
  1. Public
  2. All

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. implicit val booleanGenerator: Generator[Boolean]

    A Generator that produces Boolean values.

  6. implicit val byteGenerator: Generator[Byte]

    A Generator that produces Byte values.

  7. implicit val charGenerator: Generator[Char]

    A Generator that produces Char values.

  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  9. implicit val doubleGenerator: Generator[Double]

    A Generator that produces Double values.

  10. implicit def eitherGenerator[L, R](implicit genOfL: Generator[L], genOfR: Generator[R]): Generator[Either[L, R]]

    Given Generators for two types, L and R, this provides one for Either[L, R].

    Given Generators for two types, L and R, this provides one for Either[L, R].

    L

    the "left" type for an Either

    R

    the "right" type for an Either

    genOfL

    a Generator that produces type L

    genOfR

    a Generator that produces type R

    returns

    a Generator that produces Either[L, R]

  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  13. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  14. implicit val finiteDoubleGenerator: Generator[FiniteDouble]

    A Generator that produces Doubles, excluding infinity.

  15. implicit val finiteFloatGenerator: Generator[FiniteFloat]

    A Generator that produces Floats, excluding infinity.

  16. implicit val floatGenerator: Generator[Float]

    A Generator that produces Float values.

  17. implicit def function0Generator[T](implicit genOfT: Generator[T]): Generator[() => T]

    Given a Generator that produces values of type T, this returns one that produces functions that return a T.

    Given a Generator that produces values of type T, this returns one that produces functions that return a T.

    The functions produced here are nullary -- they take no parameters, they just spew out values of type T.

    T

    the type to produce

    genOfT

    a Generator that produces values of T

    returns

    a Generator that produces functions that return values of type T

  18. implicit def function10Generator[A, B, C, D, E, F, G, H, I, J, K](implicit genOfK: Generator[K], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K]): Generator[(A, B, C, D, E, F, G, H, I, J) => K]

    See function1Generator.

  19. implicit def function11Generator[A, B, C, D, E, F, G, H, I, J, K, L](implicit genOfL: Generator[L], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K], typeInfoL: TypeInfo[L]): Generator[(A, B, C, D, E, F, G, H, I, J, K) => L]

    See function1Generator.

  20. implicit def function12Generator[A, B, C, D, E, F, G, H, I, J, K, L, M](implicit genOfM: Generator[M], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K], typeInfoL: TypeInfo[L], typeInfoM: TypeInfo[M]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L) => M]

    See function1Generator.

  21. implicit def function13Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N](implicit genOfN: Generator[N], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K], typeInfoL: TypeInfo[L], typeInfoM: TypeInfo[M], typeInfoN: TypeInfo[N]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M) => N]

    See function1Generator.

  22. implicit def function14Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O](implicit genOfO: Generator[O], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K], typeInfoL: TypeInfo[L], typeInfoM: TypeInfo[M], typeInfoN: TypeInfo[N], typeInfoO: TypeInfo[O]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N) => O]

    See function1Generator.

  23. implicit def function15Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P](implicit genOfP: Generator[P], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K], typeInfoL: TypeInfo[L], typeInfoM: TypeInfo[M], typeInfoN: TypeInfo[N], typeInfoO: TypeInfo[O], typeInfoP: TypeInfo[P]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O) => P]

    See function1Generator.

  24. implicit def function16Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q](implicit genOfQ: Generator[Q], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K], typeInfoL: TypeInfo[L], typeInfoM: TypeInfo[M], typeInfoN: TypeInfo[N], typeInfoO: TypeInfo[O], typeInfoP: TypeInfo[P], typeInfoQ: TypeInfo[Q]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P) => Q]

    See function1Generator.

  25. implicit def function17Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R](implicit genOfR: Generator[R], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K], typeInfoL: TypeInfo[L], typeInfoM: TypeInfo[M], typeInfoN: TypeInfo[N], typeInfoO: TypeInfo[O], typeInfoP: TypeInfo[P], typeInfoQ: TypeInfo[Q], typeInfoR: TypeInfo[R]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q) => R]

    See function1Generator.

  26. implicit def function18Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S](implicit genOfS: Generator[S], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K], typeInfoL: TypeInfo[L], typeInfoM: TypeInfo[M], typeInfoN: TypeInfo[N], typeInfoO: TypeInfo[O], typeInfoP: TypeInfo[P], typeInfoQ: TypeInfo[Q], typeInfoR: TypeInfo[R], typeInfoS: TypeInfo[S]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R) => S]

    See function1Generator.

  27. implicit def function19Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T](implicit genOfT: Generator[T], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K], typeInfoL: TypeInfo[L], typeInfoM: TypeInfo[M], typeInfoN: TypeInfo[N], typeInfoO: TypeInfo[O], typeInfoP: TypeInfo[P], typeInfoQ: TypeInfo[Q], typeInfoR: TypeInfo[R], typeInfoS: TypeInfo[S], typeInfoT: TypeInfo[T]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S) => T]

    See function1Generator.

  28. implicit def function1Generator[A, B](implicit genOfB: Generator[B], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B]): Generator[(A) => B]

    Create a Generator of functions from type A to type B.

    Create a Generator of functions from type A to type B.

    Note that the generated functions are, necessarily, pretty random. In practice, the function you get from a function1Generator (and its variations, up through function22Generator) takes the hashes of its input values, combines those with a randomly-chosen number, and combines them in order to choose the generated value B.

    That said, each of the generated functions is deterministic: given the same input parameters and the same randomly-chosen number, you will always get the same B result. And the toString function on the generated function will show the formula you need to use in order to recreate that, which will look something like:

    (a: Int, b: String, c: Float) => org.scalatest.prop.valueOf[String](a, b, c)(131)

    The number and type of the a, b, c, etc, parameters, as well as the type parameter of valueOf, will on the function type you are generating, but they will always follow this pattern. valueOf is the underlying function that takes these parameters and the randomly-chosen number, and returns a value of the specified type.

    So if a property evaluation fails, the display of the generated function will tell you how to call valueOf to recreate the failure.

    The typeInfo parameters are automatically created via macros; you should generally not try to pass them manually.

    A

    the input type for the generated functions

    B

    the result type for the generated functions

    genOfB

    a Generator for the desired result type B

    typeInfoA

    automatically-created type information for type A

    typeInfoB

    automatically-created type information for type B

    returns

    a Generator that produces functions that take values of A and returns values of B

  29. implicit val function1IntToIntGenerator: Generator[(Int) => Int]

    Generate functions that take an Int and return a modified Int.

    Generate functions that take an Int and return a modified Int.

    This Generator is useful for testing edge cases of some higher-order functions. Besides obvious functions (returning the same Int, returning that Int plus 1), it tests overflow situations such as adding Int.MaxValue, negation, and other such cases.

  30. implicit def function20Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U](implicit genOfU: Generator[U], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K], typeInfoL: TypeInfo[L], typeInfoM: TypeInfo[M], typeInfoN: TypeInfo[N], typeInfoO: TypeInfo[O], typeInfoP: TypeInfo[P], typeInfoQ: TypeInfo[Q], typeInfoR: TypeInfo[R], typeInfoS: TypeInfo[S], typeInfoT: TypeInfo[T], typeInfoU: TypeInfo[U]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T) => U]

    See function1Generator.

  31. implicit def function21Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V](implicit genOfV: Generator[V], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K], typeInfoL: TypeInfo[L], typeInfoM: TypeInfo[M], typeInfoN: TypeInfo[N], typeInfoO: TypeInfo[O], typeInfoP: TypeInfo[P], typeInfoQ: TypeInfo[Q], typeInfoR: TypeInfo[R], typeInfoS: TypeInfo[S], typeInfoT: TypeInfo[T], typeInfoU: TypeInfo[U], typeInfoV: TypeInfo[V]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U) => V]

    See function1Generator.

  32. implicit def function22Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W](implicit genOfW: Generator[W], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J], typeInfoK: TypeInfo[K], typeInfoL: TypeInfo[L], typeInfoM: TypeInfo[M], typeInfoN: TypeInfo[N], typeInfoO: TypeInfo[O], typeInfoP: TypeInfo[P], typeInfoQ: TypeInfo[Q], typeInfoR: TypeInfo[R], typeInfoS: TypeInfo[S], typeInfoT: TypeInfo[T], typeInfoU: TypeInfo[U], typeInfoV: TypeInfo[V], typeInfoW: TypeInfo[W]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V) => W]

    See function1Generator.

  33. implicit def function2Generator[A, B, C](implicit genOfC: Generator[C], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C]): Generator[(A, B) => C]

    See function1Generator.

  34. implicit def function3Generator[A, B, C, D](implicit genOfD: Generator[D], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D]): Generator[(A, B, C) => D]

    See function1Generator.

  35. implicit def function4Generator[A, B, C, D, E](implicit genOfE: Generator[E], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E]): Generator[(A, B, C, D) => E]

    See function1Generator.

  36. implicit def function5Generator[A, B, C, D, E, F](implicit genOfF: Generator[F], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F]): Generator[(A, B, C, D, E) => F]

    See function1Generator.

  37. implicit def function6Generator[A, B, C, D, E, F, G](implicit genOfG: Generator[G], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G]): Generator[(A, B, C, D, E, F) => G]

    See function1Generator.

  38. implicit def function7Generator[A, B, C, D, E, F, G, H](implicit genOfH: Generator[H], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H]): Generator[(A, B, C, D, E, F, G) => H]

    See function1Generator.

  39. implicit def function8Generator[A, B, C, D, E, F, G, H, I](implicit genOfI: Generator[I], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I]): Generator[(A, B, C, D, E, F, G, H) => I]

    See function1Generator.

  40. implicit def function9Generator[A, B, C, D, E, F, G, H, I, J](implicit genOfJ: Generator[J], typeInfoA: TypeInfo[A], typeInfoB: TypeInfo[B], typeInfoC: TypeInfo[C], typeInfoD: TypeInfo[D], typeInfoE: TypeInfo[E], typeInfoF: TypeInfo[F], typeInfoG: TypeInfo[G], typeInfoH: TypeInfo[H], typeInfoI: TypeInfo[I], typeInfoJ: TypeInfo[J]): Generator[(A, B, C, D, E, F, G, H, I) => J]

    See function1Generator.

  41. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  42. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  43. implicit val intGenerator: Generator[Int]

    A Generator that produces Int values.

  44. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  45. implicit def listGenerator[T](implicit genOfT: Generator[T]): Generator[List[T]] with HavingLength[List[T]]

    Given an existing Generator[T], this creates a Generator[List[T]].

    Given an existing Generator[T], this creates a Generator[List[T]].

    T

    the type that we are producing a List of

    genOfT

    a Generator that produces values of type T

    returns

    a List of values of type T

  46. implicit val longGenerator: Generator[Long]

    A Generator that produces Long values.

  47. implicit def mapGenerator[K, V](implicit genOfTuple2KV: Generator[(K, V)]): Generator[Map[K, V]] with HavingSize[Map[K, V]]

    Given a Generator that produces Tuples of key/value pairs, this gives you one that produces Maps with those pairs.

    Given a Generator that produces Tuples of key/value pairs, this gives you one that produces Maps with those pairs.

    If you are simply looking for random pairing of the key and value types, this is pretty easy to use: if both the key and value types have Generators, then the Tuple and Map ones will be automatically and implicitly created when you need them.

    The resulting Generator also has the HavingSize trait, so you can use it to generate Maps with specific sizes.

    K

    the type of the keys for the Map

    V

    the type of the values for the Map

    genOfTuple2KV

    a Generator that produces Tuples of K and V

    returns

    a Generator of Maps from K to V

  48. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  49. implicit val negDoubleGenerator: Generator[NegDouble]

    A Generator that produces negative Doubles, excluding zero but including infinity.

  50. implicit val negFiniteDoubleGenerator: Generator[NegFiniteDouble]

    A Generator that produces negative Doubles, excluding zero and infinity.

  51. implicit val negFiniteFloatGenerator: Generator[NegFiniteFloat]

    A Generator that produces negative Floats, excluding zero and infinity.

  52. implicit val negFloatGenerator: Generator[NegFloat]

    A Generator that produces negative Floats, excluding zero but including infinity.

  53. implicit val negIntGenerator: Generator[NegInt]

    A Generator that produces negative Ints, excluding zero.

  54. implicit val negLongGenerator: Generator[NegLong]

    A Generator that produces negative Longs, excluding zero.

  55. implicit val negZDoubleGenerator: Generator[NegZDouble]

    A Generator that produces negative Doubles, including zero and infinity.

  56. implicit val negZFiniteDoubleGenerator: Generator[NegZFiniteDouble]

    A Generator that produces negative Doubles, including zero but excluding infinity.

  57. implicit val negZFiniteFloatGenerator: Generator[NegZFiniteFloat]

    A Generator that produces negative Floats, including zero but excluding infinity.

  58. implicit val negZFloatGenerator: Generator[NegZFloat]

    A Generator that produces negative Floats, including zero and infinity.

  59. implicit val negZIntGenerator: Generator[NegZInt]

    A Generator that produces negative Ints, including zero.

  60. implicit val negZLongGenerator: Generator[NegZLong]

    A Generator that produces negative Longs, including zero.

  61. implicit val nonZeroDoubleGenerator: Generator[NonZeroDouble]

    A Generator that produces Doubles, excluding zero but including infinity.

  62. implicit val nonZeroFiniteDoubleGenerator: Generator[NonZeroFiniteDouble]

    A Generator that produces Doubles, excluding zero and infinity.

  63. implicit val nonZeroFiniteFloatGenerator: Generator[NonZeroFiniteFloat]

    A Generator that produces Floats, excluding zero and infinity.

  64. implicit val nonZeroFloatGenerator: Generator[NonZeroFloat]

    A Generator that produces Floats, excluding zero but including infinity.

  65. implicit val nonZeroIntGenerator: Generator[NonZeroInt]

    A Generator that produces integers, excluding zero.

  66. implicit val nonZeroLongGenerator: Generator[NonZeroLong]

    A Generator that produces Longs, excluding zero.

  67. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  68. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  69. implicit val numericCharGenerator: Generator[NumericChar]

    A Generator that produces Chars, but only the ones that represent digits.

  70. implicit def optionGenerator[T](implicit genOfT: Generator[T]): Generator[Option[T]]

    Given a Generator for type T, this provides one for Option[T].

    Given a Generator for type T, this provides one for Option[T].

    T

    the type to generate

    genOfT

    a Generator that produces type T

    returns

    a Generator that produces Option[T]

  71. implicit def orGenerator[G, B](implicit genOfG: Generator[G], genOfB: Generator[B]): Generator[Or[G, B]]

    Given Generators for two types, G and B, this provides one for G Or B.

    Given Generators for two types, G and B, this provides one for G Or B.

    G

    the "good" type for an Or

    B

    the "bad" type for an Or

    genOfG

    a Generator that produces type G

    genOfB

    a Generator that produces type B

    returns

    a Generator that produces G Or B

  72. implicit val posDoubleGenerator: Generator[PosDouble]

    A Generator that produces positive Doubles, excluding zero but including infinity.

  73. implicit val posFiniteDoubleGenerator: Generator[PosFiniteDouble]

    A Generator that produces positive Doubles, excluding zero and infinity.

  74. implicit val posFiniteFloatGenerator: Generator[PosFiniteFloat]

    A Generator that produces positive Floats, excluding zero and infinity.

  75. implicit val posFloatGenerator: Generator[PosFloat]

    A Generator that produces positive Floats, excluding zero.

  76. implicit val posIntGenerator: Generator[PosInt]

    A Generator that produces positive integers, excluding zero.

  77. implicit val posLongGenerator: Generator[PosLong]

    A Generator that produces positive Longs, excluding zero.

  78. implicit val posZDoubleGenerator: Generator[PosZDouble]

    A Generator that produces positive Doubles, including zero and infinity.

  79. implicit val posZFiniteDoubleGenerator: Generator[PosZFiniteDouble]

    A Generator that produces positive Doubles, including zero but excluding infinity.

  80. implicit val posZFiniteFloatGenerator: Generator[PosZFiniteFloat]

    A Generator that produces positive Floats, including zero but excluding infinity.

  81. implicit val posZFloatGenerator: Generator[PosZFloat]

    A Generator that produces positive Floats, including zero and infinity.

  82. implicit val posZIntGenerator: Generator[PosZInt]

    A Generator that produces positive integers, including zero.

  83. implicit val posZLongGenerator: Generator[PosZLong]

    A Generator that produces positive Longs, including zero.

  84. implicit def setGenerator[T](implicit genOfT: Generator[T]): Generator[Set[T]] with HavingSize[Set[T]]

    Given a Generator that produces values of type T, this creates one for a Set of T.

    Given a Generator that produces values of type T, this creates one for a Set of T.

    Note that the Set type is considered to have a "size", so you can use the configuration parameters Configuration.minSize and Configuration.sizeRange to constrain the sizes of the resulting Sets when you use this Generator.

    The resulting Generator also has the HavingSize trait, so you can use it to generate Sets with specific sizes.

    T

    the type to produce

    genOfT

    a Generator that produces values of type T

    returns

    a Generator that produces Set[T].

  85. implicit val shortGenerator: Generator[Short]

    A Generator that produces Short values.

  86. implicit def sortedMapGenerator[K, V](implicit genOfTuple2KV: Generator[(K, V)], ordering: Ordering[K]): Generator[SortedMap[K, V]] with HavingSize[SortedMap[K, V]]

    Given a Generator that produces Tuples of key/value pairs, this gives you one that produces SortedMaps with those pairs.

    Given a Generator that produces Tuples of key/value pairs, this gives you one that produces SortedMaps with those pairs.

    If you are simply looking for random pairing of the key and value types, this is pretty easy to use: if both the key and value types have Generators, then the Tuple and SortedMap ones will be automatically and implicitly created when you need them.

    The resulting Generator also has the HavingSize trait, so you can use it to generate SortedMaps with specific sizes.

    K

    the type of the keys for the SortedMap

    V

    the type of the values for the SortedMap

    genOfTuple2KV

    a Generator that produces Tuples of K and V

    returns

    a Generator of SortedMaps from K to V

  87. implicit def sortedSetGenerator[T](implicit genOfT: Generator[T], ordering: Ordering[T]): Generator[SortedSet[T]] with HavingSize[SortedSet[T]]

    Given a Generator that produces values of type T, this creates one for a SortedSet of T.

    Given a Generator that produces values of type T, this creates one for a SortedSet of T.

    Note that the SortedSet type is considered to have a "size", so you can use the configuration parameters Configuration.minSize and Configuration.sizeRange to constrain the sizes of the resulting SortedSets when you use this Generator.

    The resulting Generator also has the HavingSize trait, so you can use it to generate SortedSets with specific sizes.

    T

    the type to produce

    genOfT

    a Generator that produces values of type T

    returns

    a Generator that produces SortedSet[T].

  88. implicit val stringGenerator: Generator[String]

    A Generator that produces arbitrary Strings.

    A Generator that produces arbitrary Strings.

    Note that this does not confine itself to ASCII! While failed tests will try to shrink to readable ASCII, this will produce arbitrary Unicode Strings.

  89. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  90. def toString(): String
    Definition Classes
    AnyRef → Any
  91. implicit def tuple10Generator[A, B, C, D, E, F, G, H, I, J](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J]): Generator[(A, B, C, D, E, F, G, H, I, J)]

    See tuple2Generator.

  92. implicit def tuple11Generator[A, B, C, D, E, F, G, H, I, J, K](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J], genOfK: Generator[K]): Generator[(A, B, C, D, E, F, G, H, I, J, K)]

    See tuple2Generator.

  93. implicit def tuple12Generator[A, B, C, D, E, F, G, H, I, J, K, L](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J], genOfK: Generator[K], genOfL: Generator[L]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L)]

    See tuple2Generator.

  94. implicit def tuple13Generator[A, B, C, D, E, F, G, H, I, J, K, L, M](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J], genOfK: Generator[K], genOfL: Generator[L], genOfM: Generator[M]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M)]

    See tuple2Generator.

  95. implicit def tuple14Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J], genOfK: Generator[K], genOfL: Generator[L], genOfM: Generator[M], genOfN: Generator[N]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N)]

    See tuple2Generator.

  96. implicit def tuple15Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J], genOfK: Generator[K], genOfL: Generator[L], genOfM: Generator[M], genOfN: Generator[N], genOfO: Generator[O]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O)]

    See tuple2Generator.

  97. implicit def tuple16Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J], genOfK: Generator[K], genOfL: Generator[L], genOfM: Generator[M], genOfN: Generator[N], genOfO: Generator[O], genOfP: Generator[P]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P)]

    See tuple2Generator.

  98. implicit def tuple17Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J], genOfK: Generator[K], genOfL: Generator[L], genOfM: Generator[M], genOfN: Generator[N], genOfO: Generator[O], genOfP: Generator[P], genOfQ: Generator[Q]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q)]

    See tuple2Generator.

  99. implicit def tuple18Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J], genOfK: Generator[K], genOfL: Generator[L], genOfM: Generator[M], genOfN: Generator[N], genOfO: Generator[O], genOfP: Generator[P], genOfQ: Generator[Q], genOfR: Generator[R]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R)]

    See tuple2Generator.

  100. implicit def tuple19Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J], genOfK: Generator[K], genOfL: Generator[L], genOfM: Generator[M], genOfN: Generator[N], genOfO: Generator[O], genOfP: Generator[P], genOfQ: Generator[Q], genOfR: Generator[R], genOfS: Generator[S]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S)]

    See tuple2Generator.

  101. implicit def tuple20Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J], genOfK: Generator[K], genOfL: Generator[L], genOfM: Generator[M], genOfN: Generator[N], genOfO: Generator[O], genOfP: Generator[P], genOfQ: Generator[Q], genOfR: Generator[R], genOfS: Generator[S], genOfT: Generator[T]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T)]

    See tuple2Generator.

  102. implicit def tuple21Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J], genOfK: Generator[K], genOfL: Generator[L], genOfM: Generator[M], genOfN: Generator[N], genOfO: Generator[O], genOfP: Generator[P], genOfQ: Generator[Q], genOfR: Generator[R], genOfS: Generator[S], genOfT: Generator[T], genOfU: Generator[U]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U)]

    See tuple2Generator.

  103. implicit def tuple22Generator[A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I], genOfJ: Generator[J], genOfK: Generator[K], genOfL: Generator[L], genOfM: Generator[M], genOfN: Generator[N], genOfO: Generator[O], genOfP: Generator[P], genOfQ: Generator[Q], genOfR: Generator[R], genOfS: Generator[S], genOfT: Generator[T], genOfU: Generator[U], genOfV: Generator[V]): Generator[(A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V)]

    See tuple2Generator.

  104. implicit def tuple2Generator[A, B](implicit genOfA: Generator[A], genOfB: Generator[B]): Generator[(A, B)]

    Given Generators for types A and B, get one that produces Tuples of those types.

    Given Generators for types A and B, get one that produces Tuples of those types.

    tuple2Generator (and its variants, up through tuple22Generator) will create Generators on demand for essentially arbitrary Tuples, so long as you have Generators in implicit scope for all of the component types.

    A

    the first type in the Tuple

    B

    the second type in the Tuple

    genOfA

    a Generator for type A

    genOfB

    a Generator for type B

    returns

    a Generator that produces the desired types, Tupled together.

  105. implicit def tuple3Generator[A, B, C](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C]): Generator[(A, B, C)]

    See tuple2Generator.

  106. implicit def tuple4Generator[A, B, C, D](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D]): Generator[(A, B, C, D)]

    See tuple2Generator.

  107. implicit def tuple5Generator[A, B, C, D, E](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E]): Generator[(A, B, C, D, E)]

    See tuple2Generator.

  108. implicit def tuple6Generator[A, B, C, D, E, F](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F]): Generator[(A, B, C, D, E, F)]

    See tuple2Generator.

  109. implicit def tuple7Generator[A, B, C, D, E, F, G](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G]): Generator[(A, B, C, D, E, F, G)]

    See tuple2Generator.

  110. implicit def tuple8Generator[A, B, C, D, E, F, G, H](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H]): Generator[(A, B, C, D, E, F, G, H)]

    See tuple2Generator.

  111. implicit def tuple9Generator[A, B, C, D, E, F, G, H, I](implicit genOfA: Generator[A], genOfB: Generator[B], genOfC: Generator[C], genOfD: Generator[D], genOfE: Generator[E], genOfF: Generator[F], genOfG: Generator[G], genOfH: Generator[H], genOfI: Generator[I]): Generator[(A, B, C, D, E, F, G, H, I)]

    See tuple2Generator.

  112. implicit def vectorGenerator[T](implicit genOfT: Generator[T]): Generator[Vector[T]] with HavingLength[Vector[T]]

    Given a Generator for type T, this creates one for a Vector of T.

    Given a Generator for type T, this creates one for a Vector of T.

    Note that the Vector type is considered to have a "size", so you can use the configuration parameters Configuration.minSize and Configuration.sizeRange to constrain the sizes of the resulting Vectors when you use this Generator.

    The resulting Generator also has the HavingLength trait, so you can use it to generate Vectors with specific lengths.

    T

    the type to produce

    genOfT

    a Generator that produces values of type T

    returns

    a Generator that produces values of type Vector[T]

  113. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  114. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  115. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  116. implicit def widen[T, U](genOfT: Generator[T])(implicit ev: <:<[T, U]): Generator[U]

    Allow Generators of a type to be used as Generators of a supertype.

    Allow Generators of a type to be used as Generators of a supertype.

    Given:

    • You have a type T
    • T has a supertype U
    • You have a Generator that produces values of T

    This allows you to pass that Generator[T] as a Generator[U].

    We do this instead of making Generator covariant, because then an implicit search for a Generator[U] would always be satisfied if it found just a Generator[T], and would then not generate anything except the subtype. That would be sound, but you wouldn't get a good variety of supertype values. This way, the subtype/supertype conversion is somewhat better-controlled.

    T

    the subtype that we have a Generator for

    U

    the supertype that we want a Generator for

    genOfT

    a Generator that produces values of T

    ev

    implicit evidence that T is a subtype of U

    returns

    a Generator[U] derived from the Generator[T]

Inherited from AnyRef

Inherited from Any

Ungrouped