Introduction to test.check

test.check is a tool for writing property-based tests. This differs from traditional unit-testing, where you write individual test-cases. With test.check you write universal quantifications, properties that should hold true for all input. For example, for all vectors, reversing the vector should preserve the count. Reversing it twice should equal the input. In this guide, we'll cover the thought process for coming up with properties, as well as the practice of writing the tests themselves.

A simple example

First, let's start with an example, suppose we want to test a sort function. It's easy to come up with some trivial properties for our function, namely that the output should be in ascending order. We also might want to make sure that the count of the input is preserved. Our test might look like:

(require '[clojure.test.check :as tc])
(require '[clojure.test.check.generators :as gen])
(require '[clojure.test.check.properties :as prop])

(defn ascending?
  "clojure.core/sorted? doesn't do what we might expect, so we write our
  own function"
  [coll]
  (every? (fn [[a b]] (<= a b))
          (partition 2 1 coll)))

(def property
  (prop/for-all [v (gen/vector gen/int)]
    (let [s (sort v)]
      (and (= (count v) (count s))
           (ascending? s)))))

;; test our property
(tc/quick-check 100 property)
;; => {:result true, :num-tests 100, :seed 1381894143051}

What if we were to forget to actually sort our vector? The test will fail, and then test.check will try and find 'smaller' inputs that still cause the test to fail. For example, the function might originally fail with input: [5 4 2 2 2], but test.check will shrink this down to [0 -1] (or [1 0]).

(def bad-property
  (prop/for-all [v (gen/vector gen/int)]
    (ascending? v)))

(tc/quick-check 100 bad-property)
;; => {:result false, :failing-size 7, :num-tests 8, :fail [[-2 4 -7 5 -2 7 -4]],
;; =>  :shrunk {:total-nodes-visited 19, :depth 8, :result false,
;; =>           :smallest [[0 -1]]}}

This process of shrinking is done automatically, even for our more complex generators that we write ourselves.

Generators

In order to write our property, we'll use generators. A generator knows how to generate random values for a specific type. The test.check.generators namespace has many built-in generators, as well as combinators for creating your own new generators. You can write sophisticated generators just by combining the existing generators with the given combinators. As we write generators, we can see them in practice with the sample function:

(require '[clojure.test.check.generators :as gen])

(gen/sample gen/int)
;; => (0 1 -1 0 -1 4 4 2 7 1)

we can ask for more samples:

(gen/sample gen/int 20)
;; => (0 1 1 0 2 -4 0 5 -7 -8 4 5 3 11 -9 -4 6 -5 -3 0)

or get a lazy-seq of values:

(take 1 (gen/sample-seq gen/int))
;; => 0

You may notice that as you ask for more values, the 'size' of the generated values increases. As test.check generates more values, it increases the 'size' of the generated values. This allows tests to fail early, for simple values, and only increase the size as the test continues to pass.

Compound generators

Some generators take other generators as arguments. For example the vector and list generator:

(gen/sample (gen/vector gen/nat))
;; => ([] [] [1] [1] [] [] [5 6 6 2 0 1] [3 7 5] [2 0 0 6 2 5 8] [9 1 9 3 8 3 5])

(gen/sample (gen/list gen/boolean))
;; => (() () (false) (false true false) (false true) (false true true true) (true) (false false true true) () (true))

(gen/sample (gen/map gen/keyword gen/boolean) 5)
;; => ({} {:z false} {:k true} {:v8Z false} {:9E false, :3uww false, :2s true})

Sometimes we'll want to create heterogeneous collections. The tuple generator allows us to to do this:

(gen/sample (gen/tuple gen/nat gen/boolean gen/ratio))
;; => ([0 false 0] [1 false 0] [0 false 2] [0 false -1/3] [1 true 2] [1 false 0] [2 false 3/5] [3 true -1] [3 true -5/3] [6 false 9/5])

Generator combinators

There are several generator combinators, we'll take a look at fmap, such-that and bind.

fmap

fmap allows us to create a new generator by applying a function to the values generated by another generator. Let's say we want to to create a set of natural numbers. We can create a set by calling set on a vector. So let's create a vector of natural numbers (using the nat generator), and then use fmap to call set on the values:

(gen/sample (gen/fmap set (gen/vector gen/nat)))
;; => (#{} #{1} #{1} #{3} #{0 4} #{1 3 4 5} #{0 6} #{3 4 5 7} #{0 3 4 5 7} #{1 5})

Imagine you have a record, that has a convenience creation function, foo. You can create random foos by generating the types of the arguments to foo with tuple, and then using (fmap foo (tuple ...)).

such-that

such-that allows us to create a generator that passes a predicate. Imagine we wanted to generate non-empty lists, we can use such-that to filter out empty lists:

(gen/sample (gen/such-that not-empty (gen/list gen/boolean)))
;; => ((true) (true) (false) (true false) (false) (true) (false false true true) (false) (true) (false))

bind

bind allows us to create a new generator based on the value of a previously created generator. For example, say we wanted to generate a vector of keywords, and then choose a random element from it, and return both the vector and the random element. bind takes a generator, and a function that takes a value from that generator, and creates a new generator.

(def keyword-vector (gen/such-that not-empty (gen/vector gen/keyword)))
(def vec-and-elem
  (gen/bind keyword-vector
            (fn [v] (gen/tuple (gen/elements v) (gen/return v)))))

(gen/sample vec-and-elem 4)
;; => ([:va [:va :b4]] [:Zu1 [:w :Zu1]] [:2 [:2]] [:27X [:27X :KW]])

This allows us to build quite sophisticated generators.

Record generators

Let's go through an example of generating random values of our own defrecords. Let's create a simple user record:

(defrecord User [user-name user-id email active?])

;; recall that a helper function is automatically generated
;; for us

(->User "reiddraper" 15 "reid@example.com" true)
;; #user.User{:user-name "reiddraper",
;;            :user-id 15,
;;            :email "reid@example.com",
;;            :active? true}

We can use the ->User helper function to construct our user. First, let's look at the generators we'll use for the arguments. For the user-name, we can just use an alphanumeric string, user IDs will be natural numbers, we'll construct our own simple email generator, and we'll use booleans to denote whether the user account is active. Let's write a simple email address generator:

(def domain (gen/elements ["gmail.com" "hotmail.com" "computer.org"]))
(def email-gen
  (gen/fmap (fn [[name domain-name]]
              (str name "@" domain-name))
            (gen/tuple (gen/not-empty gen/string-alphanumeric) domain)))

(last (gen/sample email-gen))
;; => "CW6161Q6@hotmail.com"

To put it all together, we'll use fmap to call our record constructor, and tuple to create a vector of the arguments:

(def user-gen
  (gen/fmap (partial apply ->User)
            (gen/tuple (gen/not-empty gen/string-alphanumeric)
                       gen/nat
                       email-gen
                       gen/boolean)))

(last (gen/sample user-gen))
;; => #user.User{:user-name "kWodcsE2",
;;               :user-id 1,
;;               :email "r2ed3VE@computer.org",
;;               :active? true}

Recursive generators

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NOTE: Writing recursive generators was significantly simplified in version 0.5.9. For the old way, see the 0.5.8 documentation.

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Writing recursive, or tree-shaped generators is easy using gen/recursive-gen. recursive-gen takes two arguments, a compound generator, and a scalar generator. We'll start with a simple example, and then move into something more complex. First, let's generate a nested vector of booleans. So our compound generator will be gen/vector and our scalar will be gen/boolean:

(def nested-vector-of-boolean (gen/recursive-gen gen/vector gen/boolean))
(last (gen/sample nested-vector-of-boolean 20))
;; => [[true] [false true true] [false]]

Now, let's make our own, JSON-like generator. We'll allow gen/list and gen/map as our compound types and gen/int and gen/boolean as our scalar types. Since recursive-gen only accepts one of each type of generator, we'll combine our compound types with a simple function, and the two scalars with gen/one-of.

(def compound (fn [inner-gen]
                  (gen/one-of [(gen/list inner-gen)
                               (gen/map inner-gen inner-gen)])))
(def scalars (gen/one-of [gen/int gen/boolean]))
(def my-json-like-thing (gen/recursive-gen compound scalars))
(last (gen/sample my-json-like-thing 20))
;; =>
;; (()
;;  {{4 -11, 1 -19} (false),
;;  {} {1 6},
;;  (false false) {true -3, false false, -7 1}})

And we see we got a list whose first element is the empty the list. The second element is a map with int keys and values. Etc.

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Check out page two for more examples of using generators in practice.