Testing

Let’s say I want to merge two sorted lists.

-- | Assuming the input lists are sorted, combine the lists into a
-- sorted output.
merge :: Ord a => [a] -> [a] -> [a]
merge (x:xs) (y:ys)
| x < y     = x : merge xs ys
| otherwise = y : merge xs ys
merge _      _      = []

Does this function work as expected? I could run a few tries in GHCi, but that’s a little unsatisfactory: I have to do the work to think up a test, but I get to use it only once. Instead, it’s much better to write the test in my file, and that way I can re-run it every time I update my merge function.

The technique described above is often referred to as unit testing and is used exensively in the real world. But is unit testing even that great? Sure you can re-run all your tests whenever you want, but that doesn’t get around the issue that you actually have to write all of the tests in the first place. Coming up with specific test cases is often tedious, repetitive, and arbitrary.

Can we do better?

# Property-based testing

Writing test cases is boring. And, it’s easy to miss out on unexpected behavior. Much better (and, more along the lines of wholemeal programming) is to define properties we wish our function to have. Then, we can get the computer to generate the test cases for us.

QuickCheck is the standard Haskell library for property-based testing. The idea is that you define a so-called property, which is then tested using pseudo-random data.

For example:

prop_numElements :: [Integer] -> [Integer] -> Bool
prop_numElements xs ys
= length xs + length ys == length (merge xs ys)

This property is saying that the sum of the lengths of the input lists should be the same as the length of the output list. (It is customary to begin property names with prop_.) Let’s try it!

*Main> quickCheck prop_numElements
*** Failed! Falsifiable (after 5 tests and 4 shrinks):
[]
[0]

(Your results may differ slightly. Remember: it’s using randomness.)

The first thing we notice is that our function is clearly wrong, with lots of stars and even an exclamation point! We then see that QuickCheck got through 5 tests before discovering the failing test case, so our function isn’t terrible. QuickCheck tells us what the failing arguments are: [] and [0]. Indeed GHCi tells us that merge [] [0] is [], which is wrong.

What’s so nice here is that QuickCheck found us such a nice, small test case to show that our function is wrong. The way it can do so is that it uses a technique called shrinking. After QuickCheck finds a test case that causes failure, it tries successively smaller and smaller arguments (according to a customizable definition of “smaller”) that keep failing. QuickCheck then reports only the smallest failing test case. This is wonderful, because otherwise the test cases QuickCheck produces would be unwieldy and hard to reason about.

(If you want to observe QuickCheck doing that, use verboseCheck instead of quickCheck.)

A final note about this property is that the type signature tells us that the property takes lists of integers, not any type a. This is so that GHC doesn’t choose a silly type to test on, such as (). We must always be careful about this when writing properties of polymorphic functions. Numbers are almost always a good choice.

## Implications

Let us take another stab at our function:

merge :: MergeFun
merge (x:xs) (y:ys)
| x < y     = x : merge2 xs (y:ys)
| otherwise = y : merge2 (x:xs) ys
merge [] ys = ys
merge xs [] = xs
*Main> quickCheck prop_numElements
+++ OK, passed 100 tests.

Huzzah!

Is that it? Are we done? Not quite. Let’s try another property:

prop_sorted :: [Integer] -> [Integer] -> Bool
prop_sorted xs ys
= merge xs ys == sort (xs ++ ys)
*Main> quickCheck prop_sorted
*** Failed! Falsifiable (after 4 tests and 3 shrinks):
[]
[1,0]

Drat. QuickCheck quite reasonably tried the list [1,0] as an input to our function. Of course, this isn’t going to work because it’s not already sorted. We need to specify an implication property:

prop_sorted :: [Integer] -> [Integer] -> Property
prop_sorted xs ys
= isSorted xs && isSorted ys ==> merge xs ys == sort (xs ++ ys)

isSorted :: Ord a => [a] -> Bool
isSorted (a:b:rest) = a <= b && isSorted (b:rest)
isSorted _          = True    -- must be fewer than 2 elements

In prop_sorted, we see the use of the operator (==>). Its type is Testable prop => Bool -> prop -> Property. It takes a Bool and a Testable thing and produces a Property. Note how prop_sorted returns a Property, not a Bool. We’ll sort these types out fully later, but I wanted to draw your attention to the appearance of Property there.

Let’s see how this works:

*Main> quickCheck prop_sorted
*** Gave up! Passed only 21 tests.

There aren’t any failures, but there aren’t a lot of successes either. We can see the problem if we use verboseCheck instead of quickCheck: QuickCheck will run the test only when both randomly-generated lists are in sorted order. The odds that a randomly-generated list of length n is sorted is 1/n!, which is generally quite small. And we need two sorted lists. This isn’t going to work out well.

## QuickCheck’s types

How does QuickCheck generate the arbitrary test cases, anyway? It uses the Arbitrary class:

class Arbitrary a where
arbitrary :: Gen a
shrink    :: a -> [a]

We’ll leave shrink to the online documentation and focus on arbitrary. The arbitrary method gives us a Gen a – a generator for the type a. Of course, the arbitrary method for lists doesn’t care about ordering (indeed, it can’t, due to parametricity), but we do. Luckily, this is a common problem, and QuickCheck offers a solution in the form of OrderedList, a wrapper around lists that have the right Arbitrary instance for our needs:

newtype OrderedList a = Ordered { getOrdered :: [a] }
instance (Ord a, Arbitrary a) => Arbitrary (OrderedList a) where ...

Now, let’s rewrite our property:

prop_sorted :: OrderedList Integer -> OrderedList Integer -> Bool
prop_sorted (Ordered xs) (Ordered ys)
= merge xs ys == sort (xs ++ ys)
*Main> quickCheck prop_sorted
+++ OK, passed 100 tests.

Huzzah! Just by changing the types a bit, we can affect instance selection to get what we want.

Yet, this all seems like black magic. How does QuickCheck do it? Let’s look more in depth at the types.

quickCheck :: Testable prop => prop -> IO ()

class Testable prop where ...
instance Testable Bool where ...
instance Testable Property where ...
instance (Arbitrary a, Show a, Testable prop) => Testable (a -> prop) where ...

We can quickCheck anything that’s Testable. Boolean values are Testable, as are the somewhat mysterious Propertys. But it’s the last instance listed here of Testable that piques our curiosity. It says that a function is Testable as long as its argument has an arbitrary method, the argument can be printed (in case of failure), and the result is Testable.

Is [Integer] -> [Integer] -> Bool Testable? Sure it is. Recall that [Integer] -> [Integer] -> Bool is equivalent to [Integer] -> ([Integer] -> Bool). Because [Integer] has both an Arbitrary instance and a Show instance, we can use the last instance above as long as [Integer] -> Bool is Testable. And that’s Testable because we (still) have an Arbitrary and a Show instance for [Integer], and Bool is Testable. So, that’s how quickCheck works – it uses the Arbitrary instances for the argument types. And, that’s how changing the argument types to OrderedList Integer got us the result we wanted.

## Generating arbitrary data

When you want to use QuickCheck over your own datatypes, it is necessary to write an Arbitrary instance for them. Here, we’ll learn how to do so.

Let’s have a look at our own list type

data List a = Empty | Entry a (List a)

instance Show a => List a where
show = show . toList

toList :: List a -> [a]
toList Empty          = []
toList (a Entry as) = a : toList as

fromList :: [a] -> List a
fromList []     = Empty
fromList (a:as) = a Entry fromList as

If we want an Arbitrary instance, we must define the arbitrary method, of type Gen (List a). Luckily for us, Gen is a monad (did you see that coming?), so some of its details are already familiar. We also realize that if we want arbitrary lists of a, we’ll need to make arbitrary as. So, our instance looks like

instance Arbitrary a => Arbitrary (List a) where
arbitrary = genList

At this point, check out the combinators available in the “Generator combinators” section of the QuickCheck documentation.

It’s helpful to think about how you, as a human, would generate an arbitrary list. One way to do it is to choose an arbitrary length (say, between 0 and 10), and then choose each element arbitrarily. Here is an implementation:

genList :: Arbitrary a => Gen (List a)
genList = do
len <- choose (0, 10)
vals <- replicateM len arbitrary
return $fromList vals Let’s try it out: *Main> sample genList [(),(),(),(),(),()] [] [(),(),(),(),(),(),(),(),()] [(),(),(),(),(),()] [(),(),(),(),(),(),(),(),()] [()] [(),(),(),(),(),(),(),()] [(),(),(),(),(),(),(),(),()] [(),(),()] [(),(),(),(),(),()] [(),(),(),(),(),(),(),(),(),()] The arbitrary lengths are working, but the element generation sure is boring. Let’s use a type annotation to spruce things up (and override GHC’s default choice of ())! *Main> sample (genList :: Gen (List Integer)) [0,0,0,0,0,0,0,0,0,0] [] [-2,3,1,0,4,-1] [-5,0,2,1,-1,-3] [-5,-6,-7,-2,-8,7,-3,4,-6] [4,-3,-3,2,-9,9] [] [10,-1] [9,-7,-16,3,15] [0,14,-1,0] [3,18,-13,-17,-20,-8] That’s better. This generation still isn’t great, though, because perhaps a function written over Lists fails only for lists longer than 10. We’d like unbounded lengths. Here’s one way to do it: genList :: Arbitrary a => Gen (List a) genList = do stop_now <- arbitrary if stop_now then return Empty else do x <- arbitrary xs <- genList return (x Entry xs) *Main> sample (genList :: Gen (List Integer)) [0,0,0,0,0,0] [] [3,-3] [] [] [-1,-1] [-10] [] [] [11] [-20,-14] The lengths are unbounded, but we’re getting a lot of empty lists. This is because at every entry in the list, there’s a 50% chance of producing Empty. That means that a list of length $$n$$ will appear only one out of $$2^n$$ times. So, lengths are unbounded, but very unlikely. The way to make progress here is to use the sized combinator. QuickCheck is set up to try “simple” arbitrary things before “complex” arbitrary things. The way it does this is using a size parameter, internal to the Gen monad. The more generating QuickCheck does, the higher this parameter gets. We want to use the size parameter to do our generation. Let’s look at the type of sized: sized :: (Int -> Gen a) -> Gen a An example is the best way of explaining how this works: genList :: Arbitrary a => Gen (List a) genList = sized$ \size -> do
len <- choose (0, size)
vals <- replicateM len arbitrary
return $fromList vals *Main> sample (genList :: Gen (List Integer)) [] [-2] [-1,3,4] [-4,-2,1,-1] [] [] [12,3,11,0,3,-12,10,5,11,12] [-4,-8,-9,2,14,5,8,11,-1,7,11,-8,2,-6] [6,10,-5,15,6] [-3,-18,-4] [9,19,13,-19] That worked nicely – the lists tend to get longer the later they appear. The idea is that sized takes a continuation: the thing to do with the size parameter. We just use a lambda function as the one argument to sized, where the lambda binds the size parameter, and then we can use it internally. If that’s too painful (say we just want to produce the size parameter, without using a continuation), you could always do something like this: getSize :: Gen Int getSize = sized return I’ll leave it to you to figure out how that works. Follow the types! As one last example, we can also choose arbitrary generators from a list based on frequency. Although the length method of genList works well for lists, the following technique is much better for trees: genList :: Arbitrary a => Gen (List a) genList = sized$ \size -> do
frequency [ (1, return Empty)
, (size, do x <- arbitrary
xs <- resize (size - 1) genList
return (x Entry xs) )]
*Main> sample (genList :: Gen (List Integer))
[]
[2,0]
[4,0,-1]
[-6,-2,3,-1,-1]
[6,6]
[2,2,2,6,6]
[-6,-9,5,-5]
[8,7,5,7,7,-1,-2,-1,-5,-3]
[15,-12,14,13,-5,-10,-9,-8,-2]
[12,-11,-8,6,-6,-4,11,11]
[-7,1,-3,4,-3,-9,4,6,-2,10,-9,-7,5,7,1]

Let’s look at the type of frequency:

frequency :: [(Int, Gen a)] -> Gen a

It takes a list of (Int, Gen a) pairs and produces a Gen a. The numbers in the list give the likelihood of choosing that element. Above, we fixed the frequency of Empty at 1, but let the likelihood of Entry vary according to the desired size. Then, in the recursive call to genList, we used resize to lower the size parameter. Otherwise, it’s too likely to get a runaway list that goes on toward infinity.

## Conclusion

Property based testing is an amazingly effective and satisfying approach to testing. It works especially well in Haskell because of the combination of purity, which means that one only needs to vary the arguments to a function, and the type system, which allows us to use type classes to very generically and composable create code that comes up with random input. QuickCheck came out in 1999 has since then inspired re-implementations in other languages as well.

# Practical hints

Very likely, your project has some code that can be tested this way, and you should do that. Here are some pointers to look at.

• QuickCheck itself.
• QuickCheck is random, which is nice. An alternative approach is to test all input, up to a given size. This way your test suite is deterministic, and at least no small corner cases remain. A library implementing that approach is SmallCheck.
• Unit tests are still useful. The default library to go for is HUnit.
• You do not want to run your tests in GHCi only. There are libraries that allow you to name and group your tests and create an executable that runs your tests and has nice shiny colored output. Check out tasty for one of those. It has support for QuickCheck, SmallCheck, HUnit and more.