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 {
  suite("clock") {
    testM("time is non-zero") {
      assertM(nanoTime, isGreaterThan(0))
    }
  }
}
    Definition Classes
    zio
  • package mock

    The mock package contains testable versions of all the standard ZIO environment types through the MockClock, MockConsole, MockSystem, and MockRandom modules.

    The mock package contains testable versions of all the standard ZIO environment types through the MockClock, MockConsole, MockSystem, and MockRandom modules. See the documentation on the individual modules for more detail about using each of them.

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

    import zio.test.mock._

myProgram.provideManaged(mockEnvironmentManaged)

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

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

    import zio.test.mock._

myProgram.provideM(MockConsole.make(MockConsole.DefaultData))

    Finally, you can create a Mock object that implements the mock interface directly using the makeMock method. This can be useful when you want to access some mocking functionality without using the environment type.

    import zio.test.mock._

for {
  mockRandom <- MockRandom.makeMock(MockRandom.DefaultData)
  n          <- mockRandom.nextInt
} yield n

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

    Definition Classes
    test
  • package reflect
    Definition Classes
    test
  • AbstractRunnableSpec
  • Assertion
  • AssertionValue
  • BoolAlgebra
  • CheckVariants
  • DefaultRunnableSpec
  • DefaultTestReporter
  • DefaultTestRunner
  • ExecutionStrategy
  • FailureDetails
  • Gen
  • GenFailureDetails
  • GenZIO
  • RenderedResult
  • RunnableSpec
  • Sample
  • Sized
  • Spec
  • TestAnnotation
  • TestAnnotationMap
  • TestAspect
  • TestExecutor
  • TestFailure
  • TestLogger
  • TestPlatform
  • TestReporter
  • TestRunner
  • TestSuccess
  • TestTimeoutException
  • TimeoutVariants
  • ZSpecSyntax
t

zio.test

CheckVariants

trait CheckVariants extends AnyRef

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  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. final def check[R, A, B, C, D](rv1: Gen[R, A], rv2: Gen[R, B], rv3: Gen[R, C], rv4: Gen[R, D])(test: (A, B, C, D) => TestResult): ZIO[R, Nothing, TestResult]

    A version of check that accepts four random variables.

  6. final def check[R, A, B, C](rv1: Gen[R, A], rv2: Gen[R, B], rv3: Gen[R, C])(test: (A, B, C) => TestResult): ZIO[R, Nothing, TestResult]

    A version of check that accepts three random variables.

  7. final def check[R, A, B](rv1: Gen[R, A], rv2: Gen[R, B])(test: (A, B) => TestResult): ZIO[R, Nothing, TestResult]

    A version of check that accepts two random variables.

  8. final def check[R, A](rv: Gen[R, A])(test: (A) => TestResult): ZIO[R, Nothing, TestResult]

    Checks the test passes for "sufficient" numbers of samples from the given random variable.

  9. final def checkAll[R, A, B, C, D](rv1: Gen[R, A], rv2: Gen[R, B], rv3: Gen[R, C], rv4: Gen[R, D])(test: (A, B, C, D) => TestResult): ZIO[R, Nothing, TestResult]

    A version of checkAll that accepts four random variables.

  10. final def checkAll[R, A, B, C](rv1: Gen[R, A], rv2: Gen[R, B], rv3: Gen[R, C])(test: (A, B, C) => TestResult): ZIO[R, Nothing, TestResult]

    A version of checkAll that accepts three random variables.

  11. final def checkAll[R, A, B](rv1: Gen[R, A], rv2: Gen[R, B])(test: (A, B) => TestResult): ZIO[R, Nothing, TestResult]

    A version of checkAll that accepts two random variables.

  12. final def checkAll[R, A](rv: Gen[R, A])(test: (A) => TestResult): ZIO[R, Nothing, TestResult]

    Checks the test passes for all values from the given random variable.

    Checks the test passes for all values from the given random variable. This is useful for deterministic Gen that comprehensively explore all possibilities in a given domain.

  13. final def checkAllM[R, E, A, B, C, D](rv1: Gen[R, A], rv2: Gen[R, B], rv3: Gen[R, C], rv4: Gen[R, D])(test: (A, B, C, D) => ZIO[R, E, TestResult]): ZIO[R, E, TestResult]

    A version of checkAllM that accepts four random variables.

  14. final def checkAllM[R, E, A, B, C](rv1: Gen[R, A], rv2: Gen[R, B], rv3: Gen[R, C])(test: (A, B, C) => ZIO[R, E, TestResult]): ZIO[R, E, TestResult]

    A version of checkAllM that accepts three random variables.

  15. final def checkAllM[R, E, A, B](rv1: Gen[R, A], rv2: Gen[R, B])(test: (A, B) => ZIO[R, E, TestResult]): ZIO[R, E, TestResult]

    A version of checkAllM that accepts two random variables.

  16. final def checkAllM[R, E, A](rv: Gen[R, A])(test: (A) => ZIO[R, E, TestResult]): ZIO[R, E, TestResult]

    Checks the effectual test passes for all values from the given random variable.

    Checks the effectual test passes for all values from the given random variable. This is useful for deterministic Gen that comprehensively explore all possibilities in a given domain.

  17. final def checkM[R, E, A, B, C, D](rv1: Gen[R, A], rv2: Gen[R, B], rv3: Gen[R, C], rv4: Gen[R, D])(test: (A, B, C, D) => ZIO[R, E, TestResult]): ZIO[R, E, TestResult]

    A version of checkM that accepts four random variables.

  18. final def checkM[R, E, A, B, C](rv1: Gen[R, A], rv2: Gen[R, B], rv3: Gen[R, C])(test: (A, B, C) => ZIO[R, E, TestResult]): ZIO[R, E, TestResult]

    A version of checkM that accepts three random variables.

  19. final def checkM[R, E, A, B](rv1: Gen[R, A], rv2: Gen[R, B])(test: (A, B) => ZIO[R, E, TestResult]): ZIO[R, E, TestResult]

    A version of checkM that accepts two random variables.

  20. final def checkM[R, E, A](rv: Gen[R, A])(test: (A) => ZIO[R, E, TestResult]): ZIO[R, E, TestResult]

    Checks the effectual test passes for "sufficient" numbers of samples from the given random variable.

  21. final def checkSome[R, A, B, C, D](rv1: Gen[R, A], rv2: Gen[R, B], rv3: Gen[R, C], rv4: Gen[R, D])(n: Int)(test: (A, B, C, D) => TestResult): ZIO[R, Nothing, TestResult]

    A version of checkSome that accepts four random variables.

  22. final def checkSome[R, A, B, C](rv1: Gen[R, A], rv2: Gen[R, B], rv3: Gen[R, C])(n: Int)(test: (A, B, C) => TestResult): ZIO[R, Nothing, TestResult]

    A version of checkSome that accepts three random variables.

  23. final def checkSome[R, A, B](rv1: Gen[R, A], rv2: Gen[R, B])(n: Int)(test: (A, B) => TestResult): ZIO[R, Nothing, TestResult]

    A version of checkSome that accepts two random variables.

  24. final def checkSome[R, A](rv: Gen[R, A])(n: Int)(test: (A) => TestResult): ZIO[R, Nothing, TestResult]

    Checks the test passes for the specified number of samples from the given random variable.

  25. final def checkSomeM[R, E, A, B, C, D](rv1: Gen[R, A], rv2: Gen[R, B], rv3: Gen[R, C], rv4: Gen[R, D])(n: Int)(test: (A, B, C, D) => ZIO[R, E, TestResult]): ZIO[R, E, TestResult]

    A version of checkSomeM that accepts four random variables.

  26. final def checkSomeM[R, E, A, B, C](rv1: Gen[R, A], rv2: Gen[R, B], rv3: Gen[R, C])(n: Int)(test: (A, B, C) => ZIO[R, E, TestResult]): ZIO[R, E, TestResult]

    A version of checkSomeM that accepts three random variables.

  27. final def checkSomeM[R, E, A, B](rv1: Gen[R, A], rv2: Gen[R, B])(n: Int)(test: (A, B) => ZIO[R, E, TestResult]): ZIO[R, E, TestResult]

    A version of checkSomeM that accepts two random variables.

  28. final def checkSomeM[R, E, A](rv: Gen[R, A])(n: Int)(test: (A) => ZIO[R, E, TestResult]): ZIO[R, E, TestResult]

    Checks the effectual test passes for the specified number of samples from the given random variable.

  29. def clone(): AnyRef
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  30. final def eq(arg0: AnyRef): Boolean
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  32. def finalize(): Unit
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  33. final def getClass(): Class[_ <: AnyRef]
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  34. def hashCode(): Int
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  35. final def isInstanceOf[T0]: Boolean
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  36. final def ne(arg0: AnyRef): Boolean
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  37. final def notify(): Unit
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  38. final def notifyAll(): Unit
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  39. final def synchronized[T0](arg0: => T0): T0
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  40. def toString(): String
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  41. final def wait(): Unit
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  42. final def wait(arg0: Long, arg1: Int): Unit
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  43. final def wait(arg0: Long): Unit
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