Packages

  • package root
    Definition Classes
    root
  • package zio
    Definition Classes
    root
  • package test

    _ZIO Test_ is a featherweight testing library for effectful programs.

    _ZIO Test_ is a featherweight testing library for effectful programs.

    The library imagines every spec as an ordinary immutable value, providing tremendous potential for composition. Thanks to tight integration with ZIO, specs can use resources (including those requiring disposal), have well- defined linear and parallel semantics, and can benefit from a host of ZIO combinators.

    import zio.test._
import zio.clock.nanoTime
import Assertion.isGreaterThan

object MyTest extends DefaultRunnableSpec {
  def spec = suite("clock")(
    testM("time is non-zero") {
      assertM(nanoTime)(isGreaterThan(0))
    }
  )
}
    Definition Classes
    zio
  • package environment

    The environment package contains testable versions of all the standard ZIO environment types through the TestClock, TestConsole, TestSystem, and TestRandom modules.

    The environment package contains testable versions of all the standard ZIO environment types through the TestClock, TestConsole, TestSystem, and TestRandom modules. See the documentation on the individual modules for more detail about using each of them.

    If you are using ZIO Test and extending RunnableSpec a TestEnvironment containing all of them will be automatically provided to each of your tests. Otherwise, the easiest way to use the test implementations in ZIO Test is by providing the TestEnvironment to your program.

    import zio.test.environment._

myProgram.provideLayer(testEnvironment)

    Then all environmental effects, such as printing to the console or generating random numbers, will be implemented by the TestEnvironment and will be fully testable. When you do need to access the "live" environment, for example to print debugging information to the console, just use the live combinator along with the effect as your normally would.

    If you are only interested in one of the test implementations for your application, you can also access them a la carte through the make method on each module. Each test module requires some data on initialization. Default data is included for each as DefaultData.

    import zio.test.environment._

myProgram.provideM(TestConsole.make(TestConsole.DefaultData))

    Finally, you can create a Test object that implements the test interface directly using the makeTest method. This can be useful when you want to access some testing functionality without using the environment type.

    import zio.test.environment._

for {
  testRandom <- TestRandom.makeTest(TestRandom.DefaultData)
  n          <- testRandom.nextInt
} yield n

    This can also be useful when you are creating a more complex environment to provide the implementation for test services that you mix in.

    Definition Classes
    test
  • package laws

    The laws package provides functionality for describing laws as values.

    The laws package provides functionality for describing laws as values. The fundamental abstraction is a set of ZLaws[Caps, R]. These laws model the laws that instances having a capability of type Caps are expected to satisfy. A capability Caps[_] is an abstraction describing some functionality that is common across different data types and obeys certain laws. For example, we can model the capability of two values of a type being compared for equality as follows:

    trait Equal[-A] {
  def equal(a1: A, a2: A): Boolean
}

    Definitions of equality are expected to obey certain laws:

    1. Reflexivity - a1 === a1 2. Symmetry - a1 === a2 ==> a2 === a1 3. Transitivity - (a1 === a2) && (a2 === a3) ==> (a1 === a3)

    These laws define what the capabilities mean and ensure that it is safe to abstract across different instances with the same capability.

    Using ZIO Test, we can represent these laws as values. To do so, we define each law using one of the ZLaws constructors. For example:

    val transitivityLaw = ZLaws.Laws3[Equal]("transitivityLaw") {
  def apply[A: Equal](a1: A, a2: A, a3: A): TestResult =
    ???
}

    We can then combine laws using the + operator:

    val reflexivityLaw: = ???
val symmetryLaw:    = ???

val equalLaws = reflexivityLaw + symmetryLaw + transitivityLaw

    Laws have a run method that takes a generator of values of type A and checks that those values satisfy the laws. In addition, objects can extend ZLawful to provide an even more convenient syntax for users to check that instances satisfy certain laws.

    object Equal extends Lawful[Equal]

object Hash extends Lawful[Hash]

object Ord extends Lawful[Ord]

checkAllLaws(Equal + Hash + Ord)(Gen.anyInt)

    Note that capabilities compose seamlessly because of contravariance. We can combine laws describing different capabilities to construct a set of laws requiring that instances having all of the capabilities satisfy each of the laws.

    Definition Classes
    test
  • package mock
    Definition Classes
    test
  • package poly
    Definition Classes
    test
  • GenFractionalPoly
  • GenIntegralPoly
  • GenNumericPoly
  • GenOrderingPoly
  • GenPoly
  • package reflect
    Definition Classes
    test

package poly

Ordering
  1. Alphabetic
Visibility
  1. Public
  2. Protected

Type Members

  1. trait GenFractionalPoly extends GenNumericPoly

    GenFractionalPoly provides evidence that instances of Gen[T] and Fractional[T] exist for some concrete but unknown type T.

  2. trait GenIntegralPoly extends GenNumericPoly

    GenIntegralPoly provides evidence that instances of Gen[T] and Integral[T] exist for some concrete but unknown type T.

  3. trait GenNumericPoly extends GenOrderingPoly

    GenNumericPoly provides evidence that instances of Gen[T] and Numeric[T] exist for some concrete but unknown type T.

  4. trait GenOrderingPoly extends GenPoly

    GenOrderingPoly provides evidence that instances of Gen[T] and Ordering[T] exist for some concrete but unknown type T.

  5. trait GenPoly extends AnyRef

    GenPoly provides evidence that an instance of Gen[T] exists for some concrete but unknown type T.

    GenPoly provides evidence that an instance of Gen[T] exists for some concrete but unknown type T. Subtypes of GenPoly provide additional constraints on the type of T, such as that an instance of Ordering[T] or Numeric[T] exists. Users can also extend GenPoly to add their own constraints.

    This allows construction of polymorphic generators where the the type is known to satisfy certain constraints even though the type itself is unknown.

    For instance, consider the following generalized algebraic data type:

    sealed trait Expr[+A] extends Product with Serializable

final case class Value[+A](value: A) extends Expr[A]
final case class Mapping[A, +B](expr: Expr[A], f: A => B) extends Expr[B]

    We would like to test that for any expression we can fuse two mappings. We want to create instances of Expr that reflect the full range of values that an Expr can take, including multiple layers of nested mappings and mappings between different types.

    Since we do not need any constraints on the generated types we can simply use GenPoly. GenPoly includes a convenient generator in its companion object, genPoly, that generates instances of 40 different types including primitive types and various collections.

    Using it we can define polymorphic generators for expressions:

    def genValue(t: GenPoly): Gen[Random with Sized, Expr[t.T]] =
  t.genT.map(Value(_))

def genMapping(t: GenPoly): Gen[Random with Sized, Expr[t.T]] =
  Gen.suspend {
    GenPoly.genPoly.flatMap { t0 =>
      genExpr(t0).flatMap { expr =>
        val genFunction: Gen[Random with Sized, t0.T => t.T] = Gen.function(t.genT)
        val genExpr1: Gen[Random with Sized, Expr[t.T]]      = genFunction.map(f => Mapping(expr, f))
        genExpr1
      }
    }
  }

def genExpr(t: GenPoly): Gen[Random with Sized, Expr[t.T]] =
  Gen.oneOf(genMapping(t), genValue(t))

    Finally, we can test our property:

    testM("map fusion") {
  check(GenPoly.genPoly.flatMap(genExpr(_))) { expr =>
    assert(eval(fuse(expr)))(equalTo(eval(expr)))
  }
}

    This will generate expressions with multiple levels of nesting and polymorphic mappings between different types, making sure that the types line up for each mapping. This provides a higher level of confidence in properties than testing with a monomorphic value.

    Inspired by Erik Osheim's presentation "Galaxy Brain: type-dependence and state-dependence in property-based testing" http://plastic-idolatry.com/erik/oslo2019.pdf.

Value Members

  1. object GenFractionalPoly
  2. object GenIntegralPoly
  3. object GenNumericPoly
  4. object GenOrderingPoly
  5. object GenPoly

Ungrouped