Random Data Generation with ScalaCheck - ScalaIO 2016

ScalaCheck is a well-known library for property-base testing. However, property-base 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 stop 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 are 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. Finally, we will show how random data generation can also be used in development to restore our data-driven-application in a particular state.

Transcript

1. RANDOM DATA GENERATION WITH SCALACHECK @DANIELASFREGOLA
2. 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: ""
5. CAN WE FIND A COMPROMISE?
6. RANDOM DATA GENERATOR GITHUB.COM/DANIELASFREGOLA/RANDOM-DATA-GENERATOR
7. RANDOM DATA GENERATOR case class Example(text: String, n: Int) val example: Example = random[Example] // Example(ਈῒ
   !䑪⡨ᵅ䎎, 73967257)
8. RANDOM DATA GENERATOR case class User(name: String, surname: String) "create a user" { val user = random[User] Post("/users", creation) ~> check { status === StatusCodes.Created assertCreation(user) } }
9. 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
10. LESS BIASED TEST DATA For every session different test data will be randomly* selected! * We can still fix the seed when needed
11. BUGS BUGS EVERYWHERE
12. 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) } }
13. 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) } }
14. HOW DOES IT WORK?
15. SCALACHECK GITHUB.COM/RICKYNILS/SCALACHECK
16. SCALACHECK-SHAPELESS ** GITHUB.COM/ALEXARCHAMBAULT/SCALACHECK-SHAPELESS Automatically infers Arbitrary[T] if: - T is a case class - T is an ADT ** but it will impact the compilation time
17. 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
18. MAKE SURE THAT THE GENERATED TEST DATA MAKES SENSE
19. CUSTOMISE YOUR ARBITRARY Before: random[String] // !!ⳘԺ Ꙑ
     After: /** Generates a string of alpha characters */ implicit val arbitraryString: Arbitrary[String] = Arbitrary(Gen.alphaStr) random[String] // hqtbonxacrmvmuMpofwtasrojjnycwuoTfkrhOpli
20. CUSTOMISE YOUR ARBITRARY case class Person(name: String, age: Int) implicit val arbitraryPerson: Arbitrary[Person] = Arbitrary { for { name <- Gen.oneOf("Daniela", "John", "Martin") age <- Gen.choose(0, 100) } yield Person(name, age) } random[Person] // Person(John,16)
21. SUMMARY > Find a compromise between using ScalaCheck and predefined fixtures > Customise the data generation to your context > Do not ignore random test failures
22. THANK YOU! > Random Data Generator: github.com/DanielaSfregola/random-data-generator > Twitter: @DanielaSfregola > Blog: danielasfregola.com