Scala Italy 2018 - Random Data Generation with ScalaCheck

ScalaCheck is a well-known library for property-base testing. However, property-based testing is not always possible when side effects are involved, for example when writing an integration test that involves data being stored in a database.

When writing non-property-base tests, we often need to initialise some data and then verify some assertions on it. However, manual data generation can make our data biased and preventing us from spotting bugs in our code.

Having our data generated randomly not only it would make our test less biased, but it will also make it a lot more readable by highlighting what part of our data is actually relevant in our test.

In this talk, we will discuss how to reuse some of the existing ScalaCheck code to generate random instances of given types and how these can be combined to generate random case classes. We will analyse the properties of a ScalaCheck generator and provide examples of how we can manipulate existing generators to meet our needs.

Transcript

1. RANDOM DATA GENERATION
   WITH SCALACHECK
   @DANIELASFREGOLA
   SCALA ITALY 2018
   

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2. HOW TO
   STEAL OTHER'S PEOPLE CODE
   AND LOOK COOL
   

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3. HOW TO
   DO OPEN SOURCE
   AND LOOK COOL
   

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5. TESTS TESTS TESTS
   

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6. BUT I WROTE UNIT TESTS FOR IT
   

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7. 100% UNIT TEST COVERAGE
   

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8. ALL DONE, IT WORKS!
   

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9. 2 UNIT TESTS - 0 INTEGRATION TESTS
   

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11. COMMON APPROACHES
    STATIC FIXTURES
    

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13. COMMON APPROACHES
    SCALACHECK
    

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14. SCALACHECK - PROPERTY BASED TESTING
    property("startsWith") = forAll { (a: String, b: String) =>
    (a+b).startsWith(a)
    }
    // + String.startsWith: OK, passed 100 tests.
    property("concatenate") = forAll { (a: String, b: String) =>
    (a+b).length > a.length && (a+b).length > b.length
    }
    // ! String.concat: Falsified after 0 passed tests.
    // > ARG_0: ""
    // > ARG_1: ""
    

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15. PROPERTY BASED TESTING - PROS
    > Test data is less biased
    > On failing,
    counter-example provided
    > Higher confidence that
    our code probably works
    

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16. PROPERTY BASED TESTING - CONS
    > Restructuring your tests
    as properties
    is not always immediate
    > Not always applicable
    with side effects
    > Configurations do affect
    the test result
    

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19. COMMON APPROACHES
    

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20. CAN WE COMPROMISE?
    

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21. CAN WE REUSE
    SOME OF THE
    SCALACHECK MAGIC?
    

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22. RANDOM DATA
    GENERATOR
    GITHUB.COM/DANIELASFREGOLA/RANDOM-DATA-GENERATOR
    

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23. RANDOM DATA GENERATOR
    case class Example(text: String, n: Int)
    val example: Example = random[Example]
    // Example(ਈ⼝ꏣᰣ∯෢ꪔ䃂ᅟ䑪⡨⿽ᵅ䎎ߐ, 73967257)
    

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24. RANDOM DATA GENERATOR
    case class User(name: String, surname: String)
    "create a user" {
    val user = random[User]
    Post("/users", user) ~> check {
    status === StatusCodes.Created
    assertCreation(user)
    }
    }
    

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25. FIX YOUR SEED
    Each session has a seed number associated
    Generating random data with seed -2481216758852790303
    Use it to debug problematic tests
    export RANDOM_DATA_GENERATOR_SEED=-2481216758852790303
    unset RANDOM_DATA_GENERATOR_SEED
    

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26. LESS BIASED TEST DATA
    For every session
    different test data
    will be randomly* selected
    * We can still fix the seed when needed!
    

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27. BUGS
    BUGS EVERYWHERE
    

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28. EASIER TO MAINTAIN
    case class User(name: String, surname: String, age: Int)
    "create a user" {
    val user = random[User]
    Post("/users", user) ~> check {
    status === Created
    assertCreated(user)
    }
    }
    

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29. IMPROVED READABILITY
    case class User(name: String, surname: String, age: Int)
    "reject user creation of an underage user" {
    val user = random[User].copy(age = 17)
    Post("/users", user) ~> check {
    status === BadRequest
    assertNotCreated(user)
    }
    }
    

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30. HOW DOES IT WORK?
    

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31. SCALACHECK1
    1 github.com/rickynils/scalacheck
    

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32. SCALACHECK-SHAPELESS2
    AUTOMATICALLY INFERS ARBITRARY[T] IF:
    > T is a case class
    > T is an sealed trait
    2 github.com/alexarchambault/scalacheck-shapeless
    

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33. LET'S LOOK AT THE CODE!
    

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34. import org.scalacheck._
    trait RandomDataGenerator extends ShapelessLike {
    private val seed = RandomDataGenerator.seed
    def random[T](implicit arb: Arbitrary[T]): T = {
    val gen: Gen[T] = arb.arbitrary
    val optT: Option[T] = gen.apply(Gen.Parameters.default, seed)
    optT.get // !!!
    }
    }
    

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35. WHY AN OPTION?
    Arbitrary {
    Gen.chooseNum(1, 100).suchThat(_ > 200)
    }
    

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36. ARBITRARY OF CUSTOM TYPE
    import java.util.Currency
    import scala.collection.JavaConversions._
    implicit val arbitraryCurrency: Arbitrary[Currency] =
    Arbitrary {
    Gen.oneOf(Currency.getAvailableCurrencies.toSeq)
    }
    random[Currency]
    // java.util.Currency = OMR
    

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37. MAKE SURE THAT
    THE GENERATED TEST DATA
    MAKES SENSE
    

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38. CUSTOMISE YOUR ARBITRARY
    Before:
    random[String]
    // ᭞❱᭟ⳘԺ〈ᦙ᠓ꍊꎼꙐႀ⤌惲
    After:
    /** Generates a string of alpha characters */
    implicit val arbitraryString: Arbitrary[String] = Arbitrary(Gen.alphaStr)
    random[String]
    // hqtbonxacrmvmuMpofwtasrojjnycwuoTfkrhOpli
    

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39. CUSTOMISE YOUR ARBITRARY
    case class Person(name: String, age: Int)
    implicit val arbitraryPerson: Arbitrary[Person] =
    Arbitrary {
    for {
    name age } yield Person(name, age)
    }
    random[Person]
    // Person(John,16)
    

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40. WHAT
    RANDOM DATA GENERATOR
    IS NOT FOR
    

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41. TYPE CLASS DERIVATION
    WITH SHAPELESS
    IMPACTS ON COMPILATION TIME
    

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42. MILES SABIN, TYPELEVEL SCALA REBOOTED, SCALAEXCHANGE 2016
    

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43. CAN WE DO BETTER?
    

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44. TYPE CLASS DERIVATION
    WITH MAGNOLIA3
    BY @PROPENSIVE
    3 github.com/propensive/magnolia
    

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45. SCALACHECK-MAGNOLIA4
    BY @ETATY
    4 github.com/etaty/scalacheck-magnolia
    

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46. 248% SPEED UP WITH MAGNOLIA !!!!!
    

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47. RANDOM DATA
    GENERATOR MAGNOLIA
    GITHUB.COM/DANIELASFREGOLA/RANDOM-DATA-GENERATOR-MAGNOLIA
    

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48. SHAPELESS
    VS
    MAGNOLIA
    

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49. WRAP UP (1)
    > A compromise between using
    ScalaCheck and predefined fixtures
    > Customise the data generation
    to your context
    > Do not ignore random test failures
    > Use it only for test purposes
    

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50. WRAP UP (2)
    > Magnolia is for faster type class derivation
    > Shapeless has more features
    > Open Source is awesome!
    

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51. Testing shows the presence,
    not the absence of bugs
    — Edsger W. Dijkstra, 1969
    

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52. THANK YOU!
    > Random Data Generator:
    github.com/DanielaSfregola/random-data-generator
    > Random Data Generator Magnolia:
    github.com/DanielaSfregola/random-data-generator-magnolia
    > Twitter: @DanielaSfregola
    > Blog: danielasfregola.com
    

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