quickcheck 0.9.2

Automatic property based testing with shrinking.
QuickCheck is a way to do property based testing using randomly generated
input. This crate comes with the ability to randomly generate and shrink
integers, floats, tuples, booleans, lists, strings, options and results.
All QuickCheck needs is a property function—it will then randomly generate
inputs to that function and call the property for each set of inputs. If the
property fails (whether by a runtime error like index out-of-bounds or by not
satisfying your property), the inputs are "shrunk" to find a smaller

The shrinking strategies for lists and numbers use a binary search to cover
the input space quickly. (It should be the same strategy used in
[Koen Claessen's QuickCheck for

[![Build status](https://github.com/BurntSushi/quickcheck/workflows/ci/badge.svg)](https://github.com/BurntSushi/quickcheck/actions)

Dual-licensed under MIT or the [UNLICENSE](https://unlicense.org).

### Documentation

The API is fully documented:

### Simple example

Here's an example that tests a function that reverses a vector:

extern crate quickcheck;

fn reverse<T: Clone>(xs: &[T]) -> Vec<T> {
    let mut rev = vec!();
    for x in xs.iter() {
        rev.insert(0, x.clone())

mod tests {
  quickcheck! {
      fn prop(xs: Vec<u32>) -> bool {
          xs == reverse(&reverse(&xs))

This example uses the `quickcheck!` macro, which is backwards compatible with
old versions of Rust.

### The `#[quickcheck]` attribute (requires Rust 1.30 or later)

To make it easier to write QuickCheck tests, the `#[quickcheck]` attribute
will convert a property function into a `#[test]` function.

To use the `#[quickcheck]` attribute, you must import the `quickcheck` macro
from the `quickcheck_macros` crate:

extern crate quickcheck;
extern crate quickcheck_macros;

mod tests {
    fn reverse<T: Clone>(xs: &[T]) -> Vec<T> {
        let mut rev = vec!();
        for x in xs {
            rev.insert(0, x.clone())

    fn double_reversal_is_identity(xs: Vec<isize>) -> bool {
        xs == reverse(&reverse(&xs))

### Installation

`quickcheck` is on `crates.io`, so you can include it in your project like so:

quickcheck = "0.9"

If you're only using `quickcheck` in your test code, then you can add it as a
development dependency instead:

quickcheck = "0.9"

If you want to use the `#[quickcheck]` attribute, then add `quickcheck_macros`

quickcheck = "0.9"
quickcheck_macros = "0.9"

N.B. When using `quickcheck` (either directly or via the attributes),
`RUST_LOG=quickcheck` enables `info!` so that it shows useful output
(like the number of tests passed). This is **not** needed to show
witnesses for failures.

Crate features:

- `"unstable"`: Enables Arbitrary implementations that require the Rust nightly
- `"use_logging"`: (Enabled by default.) Enables the log messages governed
- `"regex"`: (Enabled by default.) Enables the use of regexes with

Prior to quickcheck 0.8, this crate had an `i128` feature for enabling support
for 128-bit integers. As of quickcheck 0.8 this feature is now provided by
default and thus no longer available.

### Minimum Rust version policy

This crate's minimum supported `rustc` version is `1.34.0`.

The current policy is that the minimum Rust version required to use this crate
can be increased in minor version updates. For example, if `crate 1.0` requires
Rust 1.20.0, then `crate 1.0.z` for all values of `z` will also require Rust
1.20.0 or newer. However, `crate 1.y` for `y > 0` may require a newer minimum
version of Rust.

In general, this crate will be conservative with respect to the minimum
supported version of Rust.

With all of that said, currently, `rand` is a public dependency of
`quickcheck`. Therefore, the MSRV policy above only applies when it is more
aggressive than `rand`'s MSRV policy. Otherwise, `quickcheck` will defer to
`rand`'s MSRV policy.

### Alternative Rust crates for property testing

The [`proptest`](https://docs.rs/proptest) crate is inspired by the
[Hypothesis](https://hypothesis.works) framework for Python.
You can read a comparison between `proptest` and `quickcheck`
In particular, `proptest` improves on the concept of shrinking. So if you've
ever had problems/frustration with shrinking in `quickcheck`, then `proptest`
might be worth a try!

### Discarding test results (or, properties are polymorphic!)

Sometimes you want to test a property that only holds for a *subset* of the
possible inputs, so that when your property is given an input that is outside
of that subset, you'd discard it. In particular, the property should *neither*
pass nor fail on inputs outside of the subset you want to test. But properties
return boolean values—which either indicate pass or fail.

To fix this, we need to take a step back and look at the type of the
`quickcheck` function:

pub fn quickcheck<A: Testable>(f: A) {
    // elided

So `quickcheck` can test any value with a type that satisfies the `Testable`
trait. Great, so what is this `Testable` business?

pub trait Testable {
    fn result<G: Gen>(&self, &mut G) -> TestResult;

This trait states that a type is testable if it can produce a `TestResult`
given a source of randomness. (A `TestResult` stores information about the
results of a test, like whether it passed, failed or has been discarded.)

Sure enough, `bool` satisfies the `Testable` trait:

impl Testable for bool {
    fn result<G: Gen>(&self, _: &mut G) -> TestResult {

But in the example, we gave a *function* to `quickcheck`. Yes, functions can
satisfy `Testable` too!

impl<A: Arbitrary + Debug, B: Testable> Testable for fn(A) -> B {
    fn result<G: Gen>(&self, g: &mut G) -> TestResult {
        // elided

Which says that a function satisfies `Testable` if and only if it has a single
parameter type (whose values can be randomly generated and shrunk) and returns
any type (that also satisfies `Testable`). So a function with type `fn(usize)
-> bool` satisfies `Testable` since `usize` satisfies `Arbitrary` and `bool`
satisfies `Testable`.

So to discard a test, we need to return something other than `bool`. What if we
just returned a `TestResult` directly? That should work, but we'll need to
make sure `TestResult` satisfies `Testable`:

impl Testable for TestResult {
    fn result<G: Gen>(&self, _: &mut G) -> TestResult { self.clone() }

Now we can test functions that return a `TestResult` directly.

As an example, let's test our reverse function to make sure that the reverse of
a vector of length 1 is equal to the vector itself.

fn prop(xs: Vec<isize>) -> TestResult {
    if xs.len() != 1 {
        return TestResult::discard()
    TestResult::from_bool(xs == reverse(&xs))
quickcheck(prop as fn(Vec<isize>) -> TestResult);

(A full working program for this example is in

So now our property returns a `TestResult`, which allows us to encode a bit
more information. There are a few more
[convenience functions defined for the `TestResult`
For example, we can't just return a `bool`, so we convert a `bool` value to a

(The ability to discard tests allows you to get similar functionality as
Haskell's `==>` combinator.)

N.B. Since discarding a test means it neither passes nor fails, `quickcheck`
will try to replace the discarded test with a fresh one. However, if your
condition is seldom met, it's possible that `quickcheck` will have to settle
for running fewer tests than usual. By default, if `quickcheck` can't find
`100` valid tests after trying `10,000` times, then it will give up.
These parameters may be changed using
and [`QuickCheck::max_tests`](https://docs.rs/quickcheck/*/quickcheck/struct.QuickCheck.html#method.max_tests),
environment variables.
There is also `QUICKCHECK_MIN_TESTS_PASSED` which sets the minimum number of
valid tests that need pass (defaults to `0`) in order for it to be considered a

### Shrinking

Shrinking is a crucial part of QuickCheck that simplifies counter-examples for
your properties automatically. For example, if you erroneously defined a
function for reversing vectors as: (my apologies for the contrived example)

fn reverse<T: Clone>(xs: &[T]) -> Vec<T> {
    let mut rev = vec![];
    for i in 1..xs.len() {
        rev.insert(0, xs[i].clone())

And a property to test that `xs == reverse(reverse(xs))`:

fn prop(xs: Vec<isize>) -> bool {
    xs == reverse(&reverse(&xs))
quickcheck(prop as fn(Vec<isize>) -> bool);

Then without shrinking, you might get a counter-example like:

[quickcheck] TEST FAILED. Arguments: ([-17, 13, -12, 17, -8, -10, 15, -19,
-19, -9, 11, -5, 1, 19, -16, 6])

Which is pretty mysterious. But with shrinking enabled, you're nearly
guaranteed to get this counter-example every time:

[quickcheck] TEST FAILED. Arguments: ([0])

Which is going to be much easier to debug.

### More Thorough Checking

Quickcheck uses random input to test, so it won't
always find bugs that could be uncovered with a particular
property. You can improve your odds of finding these latent
bugs by spending more CPU cycles asking quickcheck to find
them for you. There are a few different ways to do this, and
which one you choose is mostly a matter of taste.

If you are finding yourself doing this sort of thing a
lot, you might also be interested in trying out
[`cargo fuzz`](https://github.com/rust-fuzz/cargo-fuzz),
which runs in a loop by default.

##### Running in a Loop

One approach is to run your quickcheck properties in a loop that
just keeps going until you tell it to stop or it finds a bug.
For example, you could use a bash script such as the following


while true
    cargo test qc_
    if [[ x$? != x0 ]] ; then
        exit $?

One thing to note is that this script passes the `qc_` filter to
`cargo test`. This assumes that you've prefixed all your quickcheck
properties with `qc_`. You could leave off the filter, but then
you would be running all your deterministic tests as well, which
would take time away from quickcheck!

Checking the return code and exiting is also important. Without that
test, you won't ever notice when a failure happens.

##### Cranking the Number of Tests

Another approach is to just ask quickcheck to run properties more
times. You can do this either via the
method, or via the `QUICKCHECK_TESTS` environment variable.
This will cause quickcheck to run for a much longer time. Unlike,
the loop approach this will take a bounded amount of time, which
makes it more suitable for something like a release cycle that
wants to really hammer your software.

##### Making Arbitrary Smarter

This approach entails spending more time generating interesting
inputs in your implementations of Arbitrary. The idea is to
focus on the corner cases. This approach can be tricky because
programmers are not usually great at intuiting corner cases,
and the whole idea of property checking is to take that burden
off the programmer. Despite the theoretical discomfort, this
approach can turn out to be practical.

### Generating Structs

It is very simple to generate structs in QuickCheck. Consider the following
example, where the struct `Point` is defined:

struct Point {
    x: i32,
    y: i32,

In order to generate a random `Point` instance, you need to implement
the trait `Arbitrary` for the struct `Point`:

use quickcheck::{Arbitrary, Gen};

impl Arbitrary for Point {
    fn arbitrary<G: Gen>(g: &mut G) -> Point {
        Point {
            x: i32::arbitrary(g),
            y: i32::arbitrary(g),

### Case study: The Sieve of Eratosthenes

The [Sieve of Eratosthenes](https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes)
is a simple and elegant way to find all primes less than or equal to `N`.
Briefly, the algorithm works by allocating an array with `N` slots containing
booleans. Slots marked with `false` correspond to prime numbers (or numbers
not known to be prime while building the sieve) and slots marked with `true`
are known to not be prime. For each `n`, all of its multiples in this array
are marked as true. When all `n` have been checked, the numbers marked `false`
are returned as the primes.

As you might imagine, there's a lot of potential for off-by-one errors, which
makes it ideal for randomized testing. So let's take a look at my
implementation and see if we can spot the bug:

fn sieve(n: usize) -> Vec<usize> {
    if n <= 1 {
        return vec![];

    let mut marked = vec![false; n+1];
    marked[0] = true;
    marked[1] = true;
    marked[2] = true;
    for p in 2..n {
        for i in (2*p..n).filter(|&n| n % p == 0) {
            marked[i] = true;
          .filter_map(|(i, &m)| if m { None } else { Some(i) })

Let's try it on a few inputs by hand:

sieve(3) => [2, 3]
sieve(5) => [2, 3, 5]
sieve(8) => [2, 3, 5, 7, 8] # !!!

Something has gone wrong! But where? The bug is rather subtle, but it's an
easy one to make. It's OK if you can't spot it, because we're going to use
QuickCheck to help us track it down.

Even before looking at some example outputs, it's good to try and come up with
some *properties* that are always satisfiable by the output of the function. An
obvious one for the prime number sieve is to check if all numbers returned are
prime. For that, we'll need an `is_prime` function:

fn is_prime(n: usize) -> bool {
    n != 0 && n != 1 && (2..).take_while(|i| i*i <= n).all(|i| n % i != 0)

All this is doing is checking to see if any number in `[2, sqrt(n)]` divides
`n` with base cases for `0` and `1`.

Now we can write our QuickCheck property:

fn prop_all_prime(n: usize) -> bool {

And finally, we need to invoke `quickcheck` with our property:

fn main() {
    quickcheck(prop_all_prime as fn(usize) -> bool);

A fully working source file with this code is in

The output of running this program has this message:

[quickcheck] TEST FAILED. Arguments: (4)

Which says that `sieve` failed the `prop_all_prime` test when given `n = 4`.
Because of shrinking, it was able to find a (hopefully) minimal counter-example
for our property.

With such a short counter-example, it's hopefully a bit easier to narrow down
where the bug is. Since `4` is returned, it's likely never marked as being not
prime. Since `4` is a multiple of `2`, its slot should be marked as `true` when
`p = 2` on these lines:

for i in (2*p..n).filter(|&n| n % p == 0) {
    marked[i] = true;

Ah! But does the `..` (range) operator include `n`? Nope! This particular
operator is a half-open interval.

A `2*p..n` range will never yield `4` when `n = 4`. When we change this to
`2*p..n+1`, all tests pass.

In addition, if our bug happened to result in an index out-of-bounds error,
then `quickcheck` can handle it just like any other failure—including
shrinking on failures caused by runtime errors.

But hold on... we're not done yet. Right now, our property tests that all
the numbers returned by `sieve` are prime but it doesn't test if the list is
complete. It does not ensure that all the primes between `0` and `n` are found.

Here's a property that is more comprehensive:

fn prop_prime_iff_in_the_sieve(n: usize) -> bool {
    sieve(n) == (0..(n + 1)).filter(|&i| is_prime(i)).collect::<Vec<_>>()

It tests that for each number between 0 and n, inclusive, the naive primality test
yields the same result as the sieve.

Now, if we run it:

fn main() {
    quickcheck(prop_all_prime as fn(usize) -> bool);
    quickcheck(prop_prime_iff_in_the_sieve as fn(usize) -> bool);

we see that it fails immediately for value n = 2.

[quickcheck] TEST FAILED. Arguments: (2)

If we inspect `sieve()` once again, we see that we mistakenly mark `2` as
non-prime. Removing the line `marked[2] = true;` results in both properties

### What's not in this port of QuickCheck?

I think I've captured the key features, but there are still things missing:

* As of now, only functions with 8 or fewer parameters can be quickchecked.
This limitation can be lifted to some `N`, but requires an implementation
for each `n` of the `Testable` trait.
* Functions that fail because of a stack overflow are not caught by QuickCheck.
Therefore, such failures will not have a witness attached
to them. (I'd like to fix this, but I don't know how.)
* `Coarbitrary` does not exist in any form in this package. I think it's
possible; I just haven't gotten around to it yet.