Random Data Generation with Scalacheck -- London Scala User Group 2017

https://skillsmatter.com/meetups/8847-london-scala-january-meetup

Transcript

1. RANDOM DATA GENERATION WITH SCALACHECK @DANIELASFREGOLA SCALA LONDON USER GROUP
3. COMMON APPROACHES STATIC FIXTURES
5. COMMON APPROACHES SCALACHECK
6. 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: ""
7. PROPERTY BASED TESTING - PROS > Test data is less biased > On failing, counter-example provided > Higher confidence that our code probably works
8. PROPERTY BASED TESTING - CONS > Restructuring your tests as properties is not always immediate > Not always applicable with side effects
11. COMMON APPROACHES
12. CAN WE COMPROMISE?
13. CAN WE REUSE SOME OF THE SCALACHECK MAGIC?
14. RANDOM DATA GENERATOR GITHUB.COM/DANIELASFREGOLA/RANDOM-DATA-GENERATOR
15. 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)
16. 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) } }
17. 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
18. LESS BIASED TEST DATA For every session different test data will be randomly* selected * We can still fix the seed when needed!
19. BUGS BUGS EVERYWHERE
20. 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) } }
21. 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) } }
22. HOW DOES IT WORK?
23. SCALACHECK1 1 github.com/rickynils/scalacheck
24. SCALACHECK-SHAPELESS2 AUTOMATICALLY INFERS ARBITRARY[T] IF: > T is a case class > T is an sealed trait ...BUT IT WILL IMPACT THE COMPILATION TIME 2 github.com/alexarchambault/scalacheck-shapeless
25. MILES SABIN, TYPELEVEL SCALA REBOOTED, SCALAEXCHANGE 2016
26. LET'S LOOK AT THE CODE!
27. import org.scalacheck._ trait RandomDataGenerator extends ShapelessLike { private val seed = RandomDataGenerator.seed def random[T](implicit arb: Arbitrary[T]): T = { val gen = arb.arbitrary val optT = gen.apply(Gen.Parameters.default, seed) optT.get } }
28. 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
29. MAKE SURE THAT THE GENERATED TEST DATA MAKES SENSE
30. 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
31. 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)
32. WHAT RANDOM DATA GENERATOR IS NOT FOR
33. WHAT'S NEXT?
34. WRAP UP > A compromise between using ScalaCheck and predefined fixtures > Customise the data generation to your context > Do not ignore random test failures
35. Testing shows the presence, not the absence of bugs — Edsger W. Dijkstra, 1969
36. THANK YOU! > Random Data Generator: github.com/DanielaSfregola/random-data-generator > Twitter: @DanielaSfregola > Blog: danielasfregola.com