TestRandom

When working with randomness, testing might be hard because the inputs to the tested function change on every invocation. So our code behaves in an indeterministic way.

Precisely because of this reason ZIO exposes TestRandom module which allows for fully deterministic testing of code that deals with Randomness. TestRandom can operate in two modes based on the needed use-case. It can generate a sequence of psudeo-random values using an initial seed with series of internal state transition or by feeding predefined random values.

Initial Seed with Series of Internal State Transition

In the first mode, the TestRandom is a purely functional pseudo-random number generator. It will generate pseudo-random values just like scala.util.Random. While the scala.util.Random doesn't have internal state, the TestRandom has an internal state. Instead, methods like nextInt describe state transitions from one random state to another that are automatically composed together through methods like flatMap.

The random seed can be set using setSeed and TestRandom is guaranteed to return the same sequence of values for any given seed. This is useful for deterministically generating a sequence of pseudo-random values and powers the property based testing functionality in ZIO Test:

import zio._
import zio.test.{test, _}
import zio.test.Assertion._

test("Use setSeed to generate stable values") {
  for {
    _ <- TestRandom.setSeed(27)
    r1 <- Random.nextLong
    r2 <- Random.nextLong
    r3 <- Random.nextLong
  } yield
    assertTrue(
      List(r1, r2, r3) == List[Long](
        -4947896108136290151L,
        -5264020926839611059L,
        -9135922664019402287L
      )
    )
}
// res0: Spec[Any, Nothing] = Spec(
//   caseValue = LabeledCase(
//     label = "Use setSeed to generate stable values",
//     spec = Spec(
//       caseValue = TestCase(
//         test = FlatMap(
//           trace = "repl.MdocSession.MdocApp.res0(random.md:17)",
//           first = Stateful(
//             trace = "repl.MdocSession.MdocApp.res0(random.md:17)",
//             onState = zio.ZIO$$$Lambda$18659/0x00007f994b007230@2d03ce3d
//           ),
//           successK = zio.test.package$ZTest$$$Lambda$18668/0x00007f994b00bbc8@680155f9
//         ),
//         annotations = Map(zio.test.TestAnnotation@b4aaf9ee -> List(SourceLocation(random.md,17)))
//       )
//     )
//   )
// )

Feeding Predefined Random Values

In the second mode, TestRandom maintains an internal buffer of values that can be fed with methods such as feedInts and then when random values of that type are generated they will first be taken from the buffer. This is useful for verifying that functions produce the expected output for a given sequence of random inputs.

TestRandom will automatically take values from the buffer if a value of the appropriate type is available and otherwise generate a pseudo-random value, so there is nothing we need to do to switch between the two modes. Just generate random values as we normally would to get pseudo-random values, or feed in values of our own to get those values back.

import zio._
import zio.test.{test, _}
import zio.test.Assertion._

test("One can provide its own list of ints") {
  for {
    _ <- TestRandom.feedInts(1, 9, 2, 8, 3, 7, 4, 6, 5)
    r1 <- Random.nextInt
    r2 <- Random.nextInt
    r3 <- Random.nextInt
    r4 <- Random.nextInt
    r5 <- Random.nextInt
    r6 <- Random.nextInt
    r7 <- Random.nextInt
    r8 <- Random.nextInt
    r9 <- Random.nextInt
  } yield assertTrue(
    List(1, 9, 2, 8, 3, 7, 4, 6, 5) == List(r1, r2, r3, r4, r5, r6, r7, r8, r9)
  )
}

We can also use methods like clearInts to clear the buffer of values of a given type, so we can fill the buffer with new values or go back to pseudo-random number generation.