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  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.

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  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.

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•  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.

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Companion object ConfigMap