quickcheck

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 counter-example.

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 Haskell.)

Dual-licensed under MIT or the UNLICENSE.

Documentation

The API is fully documented: https://docs.rs/quickcheck.

Simple example

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

#[cfg(test)]
#[macro_use]
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())
    }
    rev
}

#[cfg(test)]
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:

#[cfg(test)]
extern crate quickcheck;
#[cfg(test)]
#[macro_use(quickcheck)]
extern crate quickcheck_macros;

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

    #[quickcheck]
    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:

[dependencies]
quickcheck = "0.9"

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

[dev-dependencies]
quickcheck = "0.9"

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

[dev-dependencies]
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 channel.
• "use_logging": (Enabled by default.) Enables the log messages governed RUST_LOG.
• "regex": (Enabled by default.) Enables the use of regexes with env_logger.

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 crate is inspired by the Hypothesis framework for Python. You can read a comparison between proptest and quickcheck here and here. 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 {
        TestResult::from_bool(*self)
    }
}

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 examples/reverse_single.rs.)

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 type. For example, we can't just return a bool, so we convert a bool value to a TestResult.

(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 QuickCheck::tests and QuickCheck::max_tests, or by setting the QUICKCHECK_TESTS and QUICKCHECK_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 success.

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())
    }
    rev
}

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, 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 one.

#!/usr/bin/bash

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

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 tests() 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 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;
        }
    }
    marked.iter()
          .enumerate()
          .filter_map(|(i, &m)| if m { None } else { Some(i) })
          .collect()
}

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 {
    sieve(n).into_iter().all(is_prime)
}

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 examples/sieve.rs.

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 passing.

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.