quickcheck 0.2.14

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 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.)

Build status

Dual-licensed under MIT or the UNLICENSE.

Documentation

The API is fully documented: http://burntsushi.net/rustdoc/quickcheck/.

Simple example

Here's a complete working program that tests a function that reverses a vector:

extern crate quickcheck;

use quickcheck::quickcheck;

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

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

The #[quickcheck] attribute

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 enable the plugin feature and import the quickcheck_macros crate as a syntax extension:

#![feature(plugin)]
#![allow(dead_code)]
#![plugin(quickcheck_macros)]

extern crate quickcheck;

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))
}

fn main() {}

Installation

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

[dependencies]
quickcheck = "*"

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

[dev-dependencies]
quickcheck = "*"

If you want to use the #[quickcheck] attribute, then depend on quickcheck_macros instead:

[dev-dependencies]
quickcheck_macros = "*"

Note that the #[quickcheck] macro will not work when Rust 1.0 stable is released, although it will continue to work on the nightlies.

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.

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(uint) -> bool satisfies Testable since uint 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<int>) -> TestResult {
    if xs.len() != 1 {
        return TestResult::discard()
    }
    TestResult::from_bool(xs == reverse(&xs))
}
quickcheck(prop as fn(Vec<int>) -> bool);

(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. This parameter may be changed using quickcheck_config.

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<int>) -> bool {
    xs == reverse(&reverse(&xs))
}
quickcheck(prop as fn(Vec<int>) -> 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.

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<_> = (0..n+1).map(|_| false).collect();
    marked[0] = true;
    marked[1] = true;
    marked[2] = true;
    for p in 2..n {
        for i in iter::range_step(2 * p, n, p) { // whoops!
            marked[i] = true;
        }
    }
    let mut primes = vec!();
    for (i, &m) in marked.iter().enumerate() {
        if !m { primes.push(i) }
    }
    primes
}

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).iter().all(|&i| is_prime(i))
}

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

fn main() {
    quickcheck(prop_all_prime as fn(uint) -> 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 iter::range_step(2 * p, n, p) {
    marked[i] = true;
}

Ah! But does the range_step function include n? Its documentation says

Return an iterator over the range [start, stop) by step. It handles overflow by stopping.

Shucks. The range_step function will never yield 4 when n = 4. We could use n + 1, but the std::iter crate also has a range_step_inclusive which seems clearer.

Changing the call to range_step_inclusive results in sieve passing all tests for the prop_all_prime property.

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 {
    (0..(n + 1)).filter(|&i| is_prime(i)).collect() == sieve(n)
}

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(uint) -> bool);
    quickcheck(prop_prime_iff_in_the_sieve as fn(uint) -> 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 4 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.
  • The output of quickcheck does not include the name of the function it's testing. I'm not sure if this is possible or not using reflection (and this is complicated by the fact that everything is generic anyway). If not, it might be worth providing something in the public API with the ability to name functions. However, this may be moot since using #[test] will show the name of the test function.

Please let me know if I've missed anything else.

Laziness

A key aspect for writing good shrinkers is a good lazy abstraction. For this, I chose iterators. My insistence on this point has resulted in the use of an existential type, which I think I'm willing to live with.

Note though that the shrinkers for lists and integers are not lazy. Their algorithms are more complex, so it will take a bit more work to get them to use iterators like the rest of the shrinking strategies.