Writing simple-check

Nov 3, 2013

For the past several months I’ve been working on a QuickCheck (QC) library for Clojure: simple-check. In this post, we’ll look at three issues I ran into porting QC from Haskell to Clojure: typing, shrinking, and laziness. This will not act as an introduction to QC, or property-based testing. Further, this post assumes some familiarity with Haskell and Clojure.

Typing

One of the major differences between writing a QC in a statically-typed language and a dynamically-typed language is that with static-types, we get to use that information to inform QC of the generators to use to test our function. For example, if our function has the type [Int] -> Bool , Haskell QC will use this information to generate [Int] s. Furthermore, this takes advantage of the fact the we can be polymorphic on return type in Haskell. The Arbitrary type class in Haskell has a function, arbitrary , whose signature is Gen a . This allows the compiler to fill in the specialized version of Gen a for us, depending on context. In Clojure, we can only use type-based dispatch on an argument, not the return value. So, in dynamically-typed languages, we resort to explicitly specifying the generators to use for our test. Let’s see a concrete example:

In Haskell:

sortIdempotent :: [ Int ] -> Bool sortIdempotent xs = (sort xs) == (sort (sort xs)) quickCheck sortIdempotent -- +++ OK, passed 100 tests.

In Clojure:

( defn sort-idempotent ? [coll] ( = ( sort coll) ( sort ( sort coll)))) (sc/quick-check 100 (prop/for-all [coll (gen/vector gen/int)] (sort-idempotent? coll))) ;; {:result true, :num-tests 100, :seed 1383433754854}

In Erlang (also dynamically typed), using Erlang QuickCheck (EQC):

sort_idempotent( Xs ) -> lists:sort( Xs ) =:= lists:sort(lists:sort( Xs )). prop_sort_idempotent() -> ? FORALL ( Xs , list(int()), sort_idempotent( Xs )). eqc:quickcheck(prop_sort_idempotent()). %% OK, passed 100 tests

As you can see, with simple-check and Erlang QuickCheck, we have to explicitly provide the generator to use to test our function.

Shrinking

Some QC implementations have a feature called shrinking. This allows failing tests to be shrunk to ‘smaller’ failing cases, where ‘smaller’ is data-type specific, something that’d be easier for the programmer to debug. For example, if your function fails with a randomly-generated 100-element list, QC will try and remove elements, as long as the test continues to fail. In Haskell QuickCheck, random element generation and shrinking are treated separately. That is, if you want your type to shrink, you have to implement that separately from generating random values of your type. Let’s see the type class where these two functions live, Arbitrary :

class Arbitrary a where arbitrary :: Gen a -- the returned list is the first-level of the shrink tree shrink :: a -> [a] -- default implementation shrink _ = []

Most (all?) of the standard Prelude types have an Arbitrary instance already written, but you’ll need to write one for your own types. Generally you’ll write your implementation of arbitrary based on the provided generator-combinators, like choose , elements and oneof . If you want your type to shrink, you’ll have to implement this on your own. Again, this is due to the fact that value generation and shrinking are treated separately. simple-check and Erlang QuickCheck take a different approach. When you write a generator, using generator-combinators, you get shrinking ‘for free’. That’s because the notion of generating values and shrinking are tied together in these implementations. This is handy because it saves us from having to write boilerplate code to implement shrinking. Further, because it’s not nearly as common to create our own types in Clojure, let alone possible in Erlang, we don’t want to have to create our own new type solely to implement some shrink protocol. As a result, even implicit constraints in our generator are respected during shrinking. For example, suppose we write a new generator which multiplies randomly generated integers by two. This will always result in an even number being generated, and this will remain true during shrinking. This works because in simple-check, instead of the arbitrary function generating random values, we generate random values, along with the shrink tree for that value. Erlang QuickCheck is proprietary, but I imagine it works similarly. Let’s imagine how this might look using Haskell’s types:

-- a RoseTree is just an n-ary tree data RoseTree a = RoseTree a [ RoseTree a] class Arbitrary a where -- instead of generating an `a`, we generate a shrink tree of `a` arbitrary :: Gen ( RoseTree a)

The top of the tree is a randomly generated value, and its children are the first level of shrinking. Generator-combinators can then manipulate this shrink tree. Because we now act on these shrink trees, we simply create larger trees as we create more complex generators. To give a concrete example, the expression (fmap (partial * 2) gen/int) will create a new generator based on gen/int , which multiplies the randomly generated elements by two. But since this function is also applied to the children in the shrink tree, every element in the shrink tree will be multiplied by two. We can also now write generator-combinators like elements , which creates a generator by choosing a random element from a list. This generator will shrink toward choosing earlier elements in the list. Were we to use elements in our arbitrary function in Haskell QC, we’d have to write the shrinking logic ourselves. It’s important to note, however, that this is specific to Haskell QC, and not the language itself, we could’ve implemented Haskell’s QC as described here.

Laziness

Haskell QuickCheck takes advantage of whole-program laziness. For example, when shrinking, instead of traversing a tree of arguments to the function under test, and applying to values to the function the tree is traversed, we’re able to use fmap to lazily apply to function to the entire tree. We then need only traverse a tree of booleans (representing test success or failure). This allows for a higher-level of abstraction. Fortunately, Clojure lets us mimic this, as long as our types are represented as lazy sequences. To represent a large tree, we use a two-element vector, where the first element is the top value in the tree, and the second element is a lazy sequence, representing the children. Using Clojure’s lazy functions like map , filter and concat , we’re able to retain this laziness as we process the tree. However, as this tree can become large when fully-evaluated, finding bugs can be difficult. In Haskell, we’re able to find type-mistakes during compilation, whereas in Clojure we need to run our program, potentially sifting through a large tree to find our bugs, which may have been introduced several call-sites away from where we’re looking. In order to combat this, I specifically debugged with values I knew had small shrink trees, and could be easily printed at the REPL.

If you like this post, you should follow me on twitter.