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Type-directed Property Testing

> module QuickCheck where

In this lecture, we will look at QuickCheck, a technique that cleverly exploits typeclasses and monads to deliver a powerful automatic testing methodology.

Quickcheck was developed by Koen Claessen and John Hughes more than ten years ago, and has since been ported to other languages and is currently used, among other things to find subtle concurrency bugs in telecommunications code. In 2010, it received the most influential paper award for the ICFP 2000 conference.

The key idea on which QuickCheck is founded is property-based testing. That is, instead of writing individual test cases (eg unit tests corresponding to input-output pairs for particular functions) one should write properties that are desired of the functions, and then automatically generate random tests which can be run to verify (or rather, falsify) the property.

By emphasizing the importance of specifications, QuickCheck yields several benefits:

1. The developer is forced to think about what the code should do,

2. The tool finds corner-cases where the specification is violated, which leads to either the code or the specification getting fixed,

3. The specifications live on as rich, machine-checkable documentation about how the code should behave.

To use the QuickCheck library, you need to first install it with cabal.

  cabal v1-install quickcheck

> import Test.QuickCheck (Arbitrary(..),Gen(..),Property(..),OrderedList(..),
>                         forAll,frequency,elements,sized,oneof,(==>),label,
>                         quickCheck,sample,choose,quickCheckWith,
>                         classify,stdArgs,maxSuccess)
> import Control.Monad (liftM,liftM2,liftM3)
> import qualified Data.List as List
> import Data.Maybe (fromMaybe)

> import Data.Map (Map)
> import qualified Data.Map as Map

Properties

A QuickCheck property is essentially a function whose output is a boolean. A standard "hello-world" QC property might be something about common functions on lists.

> prop_revapp :: [Int] -> [Int] -> Bool
> prop_revapp xs ys = reverse (xs ++ ys) == reverse xs ++ reverse ys

That is, a property looks a bit like a mathematical theorem that the programmer believes is true. A QC convention is to use the prefix "prop_" for QC properties. Note that the type signature for the property is not the usual polymorphic signature; we have given the concrete type Int for the elements of the list. This is because QC uses the types to generate random inputs, and hence is restricted to monomorphic properties (those that don't contain type variables.)

To check a property, we simply invoke the quickCheck action with the property. Note that only certain types of properties can be tested, these properties are all in the 'Testable' type class.

quickCheck :: (Testable prop) => prop -> IO ()
  	-- Defined in Test.QuickCheck.Test

[Int] -> [Int] -> Bool is a Testable property, so let's try quickCheck on our example property above

*Main> quickCheck prop_revapp

What's that ?! Let's run the prop_revapp function on the two inputs that quickCheck identified as counter-examples. (Your counterexamples may differ from the ones below.)

*Main> prop_revapp [0] [1]

QC has found inputs for which the property function fails ie, returns False. Of course, those of you who are paying attention will realize there was a bug in our property, namely it should be

> prop_revapp_ok :: [Int] -> [Int] -> Bool
> prop_revapp_ok xs ys = reverse (xs ++ ys) == reverse ys ++ reverse xs

because reverse will flip the order of the two parts xs and ys of xs ++ ys. Now, when we run

*Main> quickCheck prop_revapp_ok

That is, Haskell generated 100 test inputs and for all of those, the property held. You can up the stakes a bit by changing the number of tests you want to run

> quickCheckN n = quickCheckWith $ stdArgs { maxSuccess = n }

and then do

*Main> quickCheckN 1000 prop_revapp_ok

QuickCheck QuickSort

Let's look at a slightly more interesting example. Here is the canonical implementation of quicksort in Haskell. (Some may quibble that this is actually the quicksort algorithm because it does not modify the list in place. But it is a reasonable purely functional analogue.)

> qsort []     = []
> qsort (x:xs) = qsort lhs ++ [x] ++ qsort rhs
>   where lhs  = [y | y <- xs, y < x]   -- this is a "list comprehension"
>                                       -- i.e. the list of all elements from
>                                       --      xs that are less than x
>         rhs  = [z | z <- xs, z > x]

Really doesn't need much explanation! Let's run it "by hand" on a few inputs

*Main> [10,9..1]

*Main> qsort [10,9..1]


*Main> [2,4..20] ++ [1,3..11]

*Main> qsort $ [2,4..20] ++ [1,3..11]

Looks good -- let's try to test that the output is in fact sorted. We need a function that checks that a list is ordered

> isOrdered :: Ord a => [a] -> Bool
> isOrdered (x:y:zs) = x <= y && isOrdered (y:zs)
> isOrdered [x] = True
> isOrdered []  = True

and then we can use the above to write a property saying that the result of qsort is an ordered list.

> prop_qsort_isOrdered :: [Int] -> Bool
> prop_qsort_isOrdered xs = isOrdered (qsort xs)

Let's test it!

*Main> quickCheckN 1000 prop_qsort_isOrdered

Conditional Properties

Here are several other properties that we might want. First, repeated qsorting should not change the list. That is,

> prop_qsort_idemp ::  [Int] -> Bool
> prop_qsort_idemp xs = qsort (qsort xs) == qsort xs

Second, the head of the result is the minimum element of the input

> prop_qsort_min :: [Int] -> Bool
> prop_qsort_min xs = head (qsort xs) == minimum xs

*Main> quickCheck prop_qsort_min

However, when we run this, we run into a glitch.

But of course! The earlier properties held for all inputs while this property makes no sense if the input list is empty! This is why thinking about specifications and properties has the benefit of clarifying the preconditions under which a given piece of code is supposed to work.

In this case we want a conditional properties where we only want the output to satisfy to satisfy the spec if the input meets the precondition that it is non-empty.

> prop_qsort_nn_min    :: [Int] -> Property
> prop_qsort_nn_min xs =
>   not (null xs) ==> head (qsort xs) == minimum xs

We can write a similar property for the maximum element too.

> prop_qsort_nn_max    :: [Int] -> Property
> prop_qsort_nn_max xs =
>   undefined

*Main> quickCheckN 100 prop_qsort_nn_min

*Main> quickCheckN 100 prop_qsort_nn_max

This time around, both the properties hold.

Note that now, instead of just being a Bool the output of the function is a Property a special type built into the QC library. Similarly the implies combinator ==> is one of many QC combinators that allow the construction of rich properties.

Testing Against a Model Implementation

We could keep writing different properties that capture various aspects of the desired functionality of qsort. Another approach for validation is to test that our qsort is behaviorally identical to a trusted reference implementation which itself may be too inefficient or otherwise unsuitable for deployment. In this case, let's use the standard library's sort function

> prop_qsort_sort    :: [Int] -> Bool
> prop_qsort_sort xs =  qsort xs == List.sort xs

which we can put to the test

*Main> quickCheckN 1000 prop_qsort_sort

Say, what?!

*Main> qsort [-1,-1]

Ugh! So close, and yet ... Can you spot the bug in our code?

qsort []     = []
qsort (x:xs) = qsort lhs ++ [x] ++ qsort rhs
  where lhs  = [y | y <- xs, y < x]
        rhs  = [z | z <- xs, z > x]

We're assuming that the only occurrence of (the value) x is itself! That is, if there are any copies of x in the tail, they will not appear in either lhs or rhs and hence they get thrown out of the output.

Is this a bug in the code? What is a bug anyway? Perhaps the fact that all duplicates are eliminated is a feature! At any rate there is an inconsistency between our mental model of how the code should behave as articulated in prop_qsort_sort and the actual behavior of the code itself.

We can rectify matters by stipulating that the qsort produces lists of distinct elements

> isDistinct :: Eq a => [a] -> Bool
> isDistinct = undefined

> prop_qsort_distinct :: [Int] -> Bool
> prop_qsort_distinct = isDistinct . qsort

and then, weakening the equivalence to only hold on inputs that are duplicate-free

> prop_qsort_distinct_sort :: [Int] -> Property
> prop_qsort_distinct_sort xs =
>   isDistinct xs ==> qsort xs == List.sort xs

QuickCheck happily checks the modified properties

*Main> quickCheck prop_qsort_distinct

*Main> quickCheck prop_qsort_distinct_sort

The Perils of Conditional Testing

Well, we managed to fix the qsort property, but beware! Adding preconditions leads one down a slippery slope. In fact, if we paid closer attention to the above runs, we would notice something

*Main> quickCheckN 10000 prop_qsort_distinct_sort
...
(5012 tests; 248 discarded)
...
+++ OK, passed 10000 tests.

The bit about some tests being discarded is ominous. In effect, when the property is constructed with the ==> combinator, QC discards the randomly generated tests on which the precondition is false. In the above case QC grinds away on the remainder until it can meet its target of 10000 valid tests. This is because the probability of a randomly generated list meeting the precondition (having distinct elements) is high enough. This may not always be the case.

The following code is (a simplified version of) the insert function from the standard library

> insert x []                 = [x]
> insert x (y:ys) | x <= y    = x : y : ys
>                 | otherwise = y : insert x ys

Given an element x and a list xs, the function walks along xs till it finds the first element greater than x and it places x to the left of that element. Thus

*Main> insert 8 ([1..3] ++ [10..13])

Indeed, the following is the well known insertion-sort algorithm

> isort :: Ord a => [a] -> [a]
> isort = foldr List.insert []

We could write our own tests, but why do something a machine can do better?!

> prop_isort_sort    :: [Int] -> Bool
> prop_isort_sort xs = isort xs == List.sort xs

*Main> quickCheckN 1000 prop_isort_sort

Now, the reason that the above works is that the insert routine preserves sorted-ness. That is, while of course the property

> prop_insert_ordered'      :: Int -> [Int] -> Bool
> prop_insert_ordered' x xs = isOrdered (insert x xs)

is bogus,

*Main> quickCheckN 1000 prop_insert_ordered'

*Main> insert 0 [0, -1]

the output is ordered if the input was ordered to begin with

> prop_insert_ordered      :: Int -> [Int] -> Property
> prop_insert_ordered x xs =
>   isOrdered xs ==> isOrdered (insert x xs)

Notice that now, the precondition is more complex -- the property requires that the input list be ordered. If we QC the property

*Main> quickCheck prop_insert_ordered

<FILL IN WHAT HAPPENS HERE!>

Aside the above example also illustrates the benefit of writing the property as p ==> q instead of using the boolean operator || to write not p || q. In the latter case, there is a flat predicate, and QC doesn't know what the precondition is, so a property may hold vacuously. For example consider the variant

> prop_insert_ordered_vacuous :: Int -> [Int] -> Bool
> prop_insert_ordered_vacuous x xs =
>   not (isOrdered xs) || isOrdered (insert x xs)

QC will happily check it for us

*Main> quickCheckN 1000 prop_insert_ordered_vacuous

Unfortunately, in the above, the tests passed vacuously only because their inputs were not ordered, and one should use ==> to avoid the false sense of security delivered by vacuity.

QC provides us with some combinators for guarding against vacuity by allowing us to investigate the distribution of test cases

label    :: String -> Property -> Property
classify :: Bool -> String -> Property -> Property

We may use these to write a property that looks like

> prop_insert_ordered_vacuous' :: Int -> [Int] -> Property
> prop_insert_ordered_vacuous' x xs =
>     label lbl $
>         not (isOrdered xs) || isOrdered (insert x xs)
>   where
>     lbl = (if isOrdered xs then "Ordered, " else "Not Ordered, ")
>           ++ show (length xs)

When we run this, we get a detailed breakdown of the 100 passing tests:

*Main> quickCheck prop_insert_ordered_vacuous'

where in the first four lines, P% COND, N means that P percent of the ordered inputs had length N, and satisfied the predicate denoted by the string COND.

What percentage of lists were ordered? How long were they?

Generating Data

Before we start discussing how QC generates data (and how we can help it generate data meeting some pre-conditions), we must ask ourselves a basic question: how does QC behave randomly in the first place?!

*Main> quickCheck prop_insert_ordered'

*Main> quickCheck prop_insert_ordered'

Eh? This seems most impure -- same inputs yielding two totally different outputs! How does that happen?

The QC library defines a type

Gen a

of "generators for values of type a".

The impurity of random generation is bottled up inside the 'Gen' type. The monad structure of this type let's us work with this impurity in a controlled way, but we will get to that. For now, note that we don't get a value of type 'a', we will do our work with these generators. If you have a generator, you can see what it produces with the sample operation:

sample :: Show a => Gen a -> IO ()

This operation generates some example values and prints them to stdout.

Generator Combinators

QC comes loaded with a set of combinators that allow us to create generators for various data structures.

The first of these combinators is choose

choose :: (System.Random.Random a) => (a, a) -> Gen a

which takes an interval and returns an random element from that interval. (The typeclass System.Random.Random describes types which can be sampled. For example, the following is a randomly chosen set of numbers between 0 and 3.

*Main> sample $ choose (0, 3)

A second useful combinator is elements

elements :: [a] -> Gen a

which returns a generator that produces values drawn from the input list

*Main> sample $ elements [10, 20..100]

A third combinator is oneof

oneof :: [Gen a] -> Gen a

which allows us to randomly choose between multiple generators

*Main> sample $ oneof [elements [10,20,30], choose (0,3)]

and finally, the above is generalized into the frequency combinator

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

which allows us to build weighted combinations of individual generators.

*Main> sample $ frequency [(1, elements [10,20]), (5, elements [11,21])]

The Generator Monad

The parameterized type 'Gen' is an instance of the monad type class. What this means (for today) is that there are a number of monadic operations available for it.

-- from the class Monad
--
return :: a -> Gen a
(>>=)  :: Gen a -> (a -> Gen b) -> Gen b

The next three are from the library Control.Monad. These are defined in terms of return and (>>=) above, so they are available for any type constructor that is an instance of the Monad class, including Gen.

liftM  :: (a -> b) -> Gen a -> Gen b
liftM2 :: (a -> b -> c) -> Gen a -> Gen b -> Gen c
liftM3 :: (a -> b -> c -> d) -> Gen a -> Gen b -> Gen c -> Gen d

Note, liftM above has another name---fmap. That's right, every monad is also a functor.

We will cover what it exactly means for Gen to be a monad in a future lecture. However, as we will see, these operations will let us put generators together compositionally.

> genThree :: Gen Int    -- a generator that always generates the value '3'
> genThree = return 3

> genPair :: Gen a -> Gen b -> Gen (a,b)
> genPair = liftM2 (,)   -- a generator for pairs

Generator Practice

Use the operators above to define generators. Make sure that you test them out with sample to make sure that they are what you want.

> genBool :: Gen Bool
> genBool = undefined

> genTriple :: Gen a -> Gen b -> Gen c -> Gen (a,b,c)
> genTriple = undefined

> genMaybe :: Gen a -> Gen (Maybe a)
> genMaybe ga = undefined

The Arbitrary Typeclass

To keep track of all these generators, QC defines a typeclass containing types for which random values can be generated!

class Arbitrary a where
  arbitrary :: Gen a

Thus, to have QC work with (ie generate random tests for) values of type a we need only make a an instance of Arbitrary by defining an appropriate arbitrary function for it. QC defines instances for base types like Int , Float, etc

*Main> sample (arbitrary :: Gen Int)

and lifts them to compound types.

instance (Arbitrary a, Arbitrary b, Arbitrary c) => Arbitrary (a,b,c) where
  arbitrary = liftM3 (,,) arbitrary arbitrary arbitrary

*Main> sample (arbitrary :: Gen (Int,Float,Bool))

*Main> sample (arbitrary :: Gen [Int])

Generating Ordered Lists

We can use the above combinators to write generators for lists

> genList1 ::  (Arbitrary a) => Gen [a]
> genList1 = liftM2 (:) arbitrary genList1

*Main> sample (genList1 :: Gen [Int])

Can you spot a problem in the above?

Let's try again,

> genList2 ::  (Arbitrary a) => Gen [a]
> genList2 = oneof [ return []
>                  , liftM2 (:) arbitrary genList2]

*Main> sample (genList2 :: Gen [Int])

This is not bad, but there is still something undesirable. What is wrong with this output?

This version fixes the problem. We only choose [] one eighth of the time.

> genList3 ::  (Arbitrary a) => Gen [a]
> genList3 = frequency [ (1, return [])
>                      , (7, liftM2 (:) arbitrary genList3) ]

*Main> sample (genList3 :: Gen [Int])

However, genList3 has the opposite problem --- it generates a lot of long lists (longer than length 2 or 3) but not so many short ones. But finding bugs with shorter lists is a lot faster than finding bugs with long lists.

So, two last tweaks. We let quickcheck determine what frequency to use, and we decrease the frequency of cons with each recursive call. For the former, we rely on the following function from QC library.

     sized :: (Int -> Gen a) -> Gen a

This function is higher-order; it takes a generator with a size parameter and uses it to develop a new generator by progressively increasing this size.

For the latter, when we define this "size-aware" function, we cut the size in half for each recursive call.

> genList4 ::  (Arbitrary a) => Gen [a]
> genList4 = sized gen where
>  gen n = frequency [ (1, return [])
>                    , (n, liftM2 (:) arbitrary (gen (n `div` 2))) ]

Now look at that distribution! Not too small, not too big, not too many nulls.

*Main> sample (genList4 :: Gen [Int])

I encourage you to look at the implementation of genList4 closely. This use of frequency and sized is particularly important to controlling the generation of tree-structured data.

For practice, see if you can generate arbitrary trees using the pattern shown above in genList4.

> data Tree a = Empty | Branch a (Tree a) (Tree a) deriving (Show)

> instance Arbitrary a => Arbitrary (Tree a) where
>     arbitrary = sized gen where
>       gen n = undefined

*Main> sample (arbitrary :: Gen (Tree Int))

Generating data that satisfies properties

We can use the above to build a custom generator that always returns ordered lists by mapping the sort function over the generated list.

> genOrdList :: (Arbitrary a, Ord a) => Gen [a]
> genOrdList = fmap List.sort genList3

*Main> sample (genOrdList :: Gen [Int])

NOTE: Above, just saying sort genList3 doesn't work. We have that genList3 is a generator for lists, not a list itself. Because Gen is a functor, the right way to compose generation with a transformation is to use fmap.

To check the output of a custom generator we can use the forAll combinator

forAll :: (Show a, Testable prop) => Gen a -> (a -> prop) -> Property

For example, we can check that in fact, the combinator only produces ordered lists

*Main> quickCheck $ forAll genOrdList isOrdered

and now, we can properly test the insert property

> prop_insert :: Int -> Property
> prop_insert x = forAll genOrdList $ \xs ->
>   isOrdered xs && isOrdered (insert x xs)

*Main> quickCheck prop_insert

Using newtype for smarter test-case generation

This works very well, but we might not want to write forAll genOrdList everywhere we want to test a property on ordered lists only. In order to get around that, we can define a new type that wraps lists, but has a different Arbitrary instance:

> newtype OrdList a = OrdList [a] deriving (Eq, Ord, Show, Read)

> instance (Ord a, Arbitrary a) => Arbitrary (OrdList a) where
>   arbitrary = fmap OrdList genOrdList

This says that to generate an arbitrary OrdList, we use the genOrdList generator we just defined, and package that up.

*Main> sample (arbitrary :: Gen (OrdList Int))

Now, we can rewrite our prop_insert function more simply:

> prop_insert' :: Int -> OrdList Int -> Bool
> prop_insert' x (OrdList xs) = isOrdered $ insert x xs

And in fact, QuickCheck already has this type built in:

> prop_insert'' :: Int -> OrderedList Int -> Bool
> prop_insert'' x (Ordered xs) = isOrdered $ insert x xs

This technique of using newtypes for special-purpose instances is very common, both in QuickCheck and in other Haskell libraries.

QuickCheck outside of Haskell

As a testing tool, QuickCheck has been ported to many languages, some of which are listed on its wikipedia page 13. Haskell's type classes (and monads) mean that the implementation of QuickCheck in Haskell is surprisingly simple.

Credit: This lecture based on 12.