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_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)) } } }
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.