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UserGuide of scalacheck project mentioned sized generators. The explanation code 

def matrix[T](g:Gen[T]):Gen[Seq[Seq[T]]] = Gen.sized {size => val side = scala.Math.sqrt(size).asInstanceOf[Int] //little change to prevent compile-time exception Gen.vectorOf(side, Gen.vectorOf(side, g)) }

explained nothing for me. After some exploration I understood that length of generated sequence does not depend on actual size of generator (there is resize method in Gen object that "Creates a resized version of a generator" according to javadoc (maybe that means something different?)).

val g = Gen.choose(1,5) val g2 = Gen.resize(15, g) println(matrix(g).sample) // (1) println(matrix(g2).sample) // (2) //1,2 produce Seq with same length

Could you explain me what had I missed and give me some examples how you use them in testing code?

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The vectorOf (which now is replaced with listOf) generates lists with a size that depends (linearly) on the size parameter that ScalaCheck sets when it evaluates a generator. When ScalaCheck tests a property it will increase this size parameter for each test, resulting in properties that are tested with larger and larger lists (if listOf is used).

If you create a matrix generator by just using the listOf generator in a nested fashion, you will get matrices with a size that depends on the square of the size parameter. Hence when using such a generator in a property you might end up with very large matrices, since ScalaCheck increases the size parameter for each test run. However, if you use the resize generator combinator in the way it is done in the ScalaCheck User Guide, your final matrix size depend linearly on the size parameter, resulting in nicer performance when testing your properties.

You should really not have to use the resize generator combinator very often. If you need to generate lists that are bounded by some specific size, it's much better to do something like the example below instead, since there is no guarantee that the listOf/ containerOf generators really use the size parameter the way you expect.

def genBoundedList(maxSize: Int, g: Gen[T]): Gen[List[T]] = { Gen.choose(0, maxSize) flatMap { sz => Gen.listOfN(sz, g) } }
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This led me to Gen.frequency((10, 2), (5, 3), (1, 1)).flatMap(Gen.listOfN(_, names)) which was exactly what I needed!
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The vectorOf method that you use is deprecated , and you should use the listOf method. This generates a list of random length where the maximum length is limited by the size of the generator. You should therefore resize the generator that actually generates the actual list if you want control over the maximum elements that are generated:

 scala> val g1 = Gen.choose(1,5) g1: org.scalacheck.Gen[Int] = Gen() scala> val g2 = Gen.listOf(g1) g2: org.scalacheck.Gen[List[Int]] = Gen() scala> g2.sample res19: Option[List[Int]] = Some(List(4, 4, 4, 4, 2, 4, 2, 3, 5, 1, 1, 1, 4, 4, 1, 1, 4, 5, 5, 4, 3, 3, 4, 1, 3, 2, 2, 4, 3, 4, 3, 3, 4, 3, 2, 3, 1, 1, 3, 2, 5, 1, 5, 5, 1, 5, 5, 5, 5, 3, 2, 3, 1, 4, 3, 1, 4, 2, 1, 3, 4, 4, 1, 4, 1, 1, 4, 2, 1, 2, 4, 4, 2, 1, 5, 3, 5, 3, 4, 2, 1, 4, 3, 2, 1, 1, 1, 4, 3, 2, 2)) scala> val g3 = Gen.resize(10, g2) g3: java.lang.Object with org.scalacheck.Gen[List[Int]] = Gen() scala> g3.sample res0: Option[List[Int]] = Some(List(1)) scala> g3.sample res1: Option[List[Int]] = Some(List(4, 2)) scala> g3.sample res2: Option[List[Int]] = Some(List(2, 1, 2, 4, 5, 4, 2, 5, 3))
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