Random Data Generation with ScalaCheck - Scalar 2017

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 initialize 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 SCALAR 2017
2. TESTS TESTS TESTS
4. COMMON APPROACHES STATIC FIXTURES
5. HM, WORKED IN TESTS WHEN I POURED WATER DIRECTLY INTO DRAIN BY EMILBRONIKOWSKI
6. COMMON APPROACHES SCALACHECK 1 1 github.com/rickynils/scalacheck
7. 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: ""
8. PROPERTY BASED TESTING - PROS > Test data is less biased > On failing, counter-example provided > Higher confidence that our code probably works
9. PROPERTY BASED TESTING - CONS > Not always immediate > Configurations do affect the test result > Not always applicable with side effects
13. COMMON APPROACHES
14. CAN WE COMPROMISE?
15. LET'S DO IT! def random[T]: T = ???
16. CAN WE REUSE SOME SCALACHECK MAGIC?
17. Gen[T] package org.scalacheck sealed abstract class Gen[T] { def sample: Option[T] = apply(Gen.Parameters.default, Seed.random()) }
18. RANDOM DATA GENERATOR - WIP import org.scalacheck.Gen def random[T]: T = { val gen: Gen[T] = ??? val optT: Option[T] = gen.sample optT.get }
19. Arbitrary[T] package org.scalacheck sealed abstract class Arbitrary[T] { val arbitrary: Gen[T] }
20. RANDOM DATA GENERATOR - WIP import org.scalacheck.{Arbitrary, Gen} def random[T](implicit arb: Arbitrary[T]): T = { val gen: Gen[T] = arb.arbitrary val optT: Option[T] = gen.sample optT.get }
21. SCALACHECK-SHAPELESS 2 AUTOMATICALLY INFERS ARBITRARY[T] IF: > T is a case class > T is an sealed trait 2 github.com/alexarchambault/scalacheck-shapeless
22. RANDOM DATA GENERATOR - DONE? import org.scalacheck.{Arbitrary, Gen} import org.scalacheck.Shapeless._ def random[T](implicit arb: Arbitrary[T]): T = { val gen: Gen[T] = arb.arbitrary val optT: Option[T] = gen.sample optT.get }
23. RANDOM DATA GENERATOR scala> random[String] res0: String = ح㻞ꔛᵏ⌧㈽フᲆ哃᩠ ꕸẃḷ╏䉁 scala> random[Int] res1: Int = 2147483647 scala> random[Int] res2: Int = -407671469 scala> case class Bro(a: String, b: Double) defined class Bro scala> random[Bro] res3: Bro = Bro(ꌏ ࠞ ໐㞸㣣ߖ啾࿏奤ಧ 㞧㽡Ṥᘭ!㭾梖䅱ϩ⅖๳☥梠,-2.4322029262034435E58
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) } }
25. CAN WE MAKE TESTS DETERMINISTIC ON DEMAND?
26. 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
27. Gen[T] package org.scalacheck sealed abstract class Gen[T] { def sample: Option[T] = apply(Gen.Parameters.default, Seed.random()) }
28. RANDOM DATA GENERATOR - DONE! import org.scalacheck.Shapeless._ val seedNum: Long = ??? def random[T](implicit arb: Arbitrary[T]): T = { val gen: Gen[T] = arb.arbitrary val optT: Option[T] = gen.apply(Gen.Parameters.default, Seed(seedNum)) optT.get }
29. RANDOM DATA GENERATOR GITHUB.COM/DANIELASFREGOLA/RANDOM-DATA-GENERATOR
30. RANDOM DATA GENERATOR PROS
31. 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) } }
32. 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) } }
33. LESS BIASED TEST DATA For every session different test data will be randomly* selected * We can still fix the seed when needed!
34. BUGS BUGS EVERYWHERE
35. RANDOM DATA GENERATOR LESSONS LEARNED
36. SCALACHECK-SHAPELESS IS NOT ALWAYS ENOUGH
37. 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
38. MAKE SURE THAT THE GENERATED TEST DATA MAKES SENSE
39. CUSTOMISE YOUR ARBITRARY random[String] > ᭞❱᭟ⳘԺ〈ᦙ᠓ꍊꎼꙐႀ⤌惲 /** Generates a string of alpha characters */ implicit val arb: Arbitrary[String] = Arbitrary(Gen.alphaStr) random[String] > hqtbonxacrmvmuMpofwtasrojjnycwuoTfkrhOpli
40. 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)
41. SHAPELESS impacts on COMPILATION TIME
42. MILES SABIN, TYPELEVEL SCALA REBOOTED, SCALAEXCHANGE 2016
43. INDUCTIVE HEURISTICS TO THE RESCUE! Faster compilation of inductive implicits > Typelevel Scala [ #129 - Merged ] > Lightbend Scala [ #5649 - Open ]
44. CACHING Arbitrary[T] HELPS
45. shapeless.cachedImplicit import shapeless._ object CachedArbitraryImplicits { implicit val arbA: Arbitrary[A] = cachedImplicit implicit val arbB: Arbitrary[B] = cachedImplicit }
46. RANDOM DATA GENERATOR IS FOR TESTING
47. WRAP UP > A compromise between test strategies > Customise your data generation > Meant for testing > Do not ignore random test failures
48. Testing shows the presence, not the absence of bugs — Edsger W. Dijkstra, 1969
49. THANK YOU! > Random Data Generator: github.com/DanielaSfregola/random-data-generator > Twitter: @DanielaSfregola > Blog: danielasfregola.com