Introduction

Proptest is a property testing framework (i.e., the QuickCheck family) inspired by the Hypothesis framework for Python. It allows to test that certain properties of your code hold for arbitrary inputs, and if a failure is found, automatically finds the minimal test case to reproduce the problem. Unlike QuickCheck, generation and shrinking is defined on a per-value basis instead of per-type, which makes it more flexible and simplifies composition.

Status of this crate

The crate is fairly close to being feature-complete and has not seen substantial architectural changes in quite some time. At this point, it mainly sees passive maintenance.

See the changelog for a full list of substantial historical changes, breaking and otherwise.

What is property testing?

Property testing is a system of testing code by checking that certain properties of its output or behaviour are fulfilled for all inputs. These inputs are generated automatically, and, critically, when a failing input is found, the input is automatically reduced to a minimal test case.

Property testing is best used to complement traditional unit testing (i.e., using specific inputs chosen by hand). Traditional tests can test specific known edge cases, simple inputs, and inputs that were known in the past to reveal bugs, whereas property tests will search for more complicated inputs that cause problems.

The proptest crate

The proptest crate provides most of Proptest’s functionality, including most strategies and the testing framework itself.

This part of the book is dedicated to introductory material, such as tutorials, and general usage suggestions. It does not contain reference documentation; for that, please see the rustdoc documentation.

Getting Started

Let’s say we want to make a function that parses dates of the form YYYY-MM-DD. We’re not going to worry about validating the date, any triple of integers is fine. So let’s bang something out real quick.

#![allow(unused)]
fn main() {
fn parse_date(s: &str) -> Option<(u32, u32, u32)> {
    if 10 != s.len() { return None; }
    if "-" != &s[4..5] || "-" != &s[7..8] { return None; }

    let year = &s[0..4];
    let month = &s[6..7];
    let day = &s[8..10];

    year.parse::<u32>().ok().and_then(
        |y| month.parse::<u32>().ok().and_then(
            |m| day.parse::<u32>().ok().map(
                |d| (y, m, d))))
}
}

It compiles, that means it works, right? Maybe not, let’s add some tests.

fn parse_date(s: &str) -> Option<(u32, u32, u32)> {
    if 10 != s.len() { return None; }
    if "-" != &s[4..5] || "-" != &s[7..8] { return None; }

    let year = &s[0..4];
    let month = &s[6..7];
    let day = &s[8..10];

    year.parse::<u32>().ok().and_then(
        |y| month.parse::<u32>().ok().and_then(
            |m| day.parse::<u32>().ok().map(
                |d| (y, m, d))))
}
#[test]
fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
fn test_parse_date() {
    assert_eq!(None, parse_date("2017-06-1"));
    assert_eq!(None, parse_date("2017-06-170"));
    assert_eq!(None, parse_date("2017006-17"));
    assert_eq!(None, parse_date("2017-06017"));
    assert_eq!(Some((2017, 06, 17)), parse_date("2017-06-17"));
}
fn main() { test_parse_date(); }

Tests pass, deploy to production! But now your application starts crashing, and people are upset that you moved Christmas to February. Maybe we need to be a bit more thorough.

In Cargo.toml, add

[dev-dependencies]
proptest = "1.0.0"

Now we can add some property tests to our date parser. But how do we test the date parser for arbitrary inputs, without making another date parser in the test to validate it? We won’t need to as long as we choose our inputs and properties correctly. But before correctness, there’s actually an even simpler property to test: The function should not crash. Let’s start there.

extern crate proptest;
// Bring the macros and other important things into scope.
use proptest::prelude::*;
fn parse_date(s: &str) -> Option<(u32, u32, u32)> {
    if 10 != s.len() { return None; }
    if "-" != &s[4..5] || "-" != &s[7..8] { return None; }

    let year = &s[0..4];
    let month = &s[6..7];
    let day = &s[8..10];

    year.parse::<u32>().ok().and_then(
        |y| month.parse::<u32>().ok().and_then(
            |m| day.parse::<u32>().ok().map(
                |d| (y, m, d))))
}
proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn doesnt_crash(s in "\\PC*") {
        parse_date(&s);
    }
}
fn main() { doesnt_crash(); }

What this does is take a literally random &String (ignore \\PC* for the moment, we’ll get back to that — if you’ve already figured it out, contain your excitement for a bit) and give it to parse_date() and then throw the output away.

When we run this, we get a bunch of scary-looking output, eventually ending with

thread 'main' panicked at 'Test failed: byte index 4 is not a char boundary; it is inside 'ௗ' (bytes 2..5) of `aAௗ0㌀0`; minimal failing input: s = "aAௗ0㌀0"
	successes: 102
	local rejects: 0
	global rejects: 0
'

If we look at the top directory after the test fails, we’ll see a new proptest-regressions directory, which contains some files corresponding to source files containing failing test cases. These are failure persistence files. The first thing we should do is add these to source control.

$ git add proptest-regressions

The next thing we should do is copy the failing case to a traditional unit test since it has exposed a bug not similar to what we’ve tested in the past.

fn parse_date(s: &str) -> Option<(u32, u32, u32)> {
    if 10 != s.len() { return None; }
    if "-" != &s[4..5] || "-" != &s[7..8] { return None; }

    let year = &s[0..4];
    let month = &s[6..7];
    let day = &s[8..10];

    year.parse::<u32>().ok().and_then(
        |y| month.parse::<u32>().ok().and_then(
            |m| day.parse::<u32>().ok().map(
                |d| (y, m, d))))
}
#[test]
fn dummy() {} // Doctests don't build `#[test]` functions, so we need this
fn test_unicode_gibberish() {
    assert_eq!(None, parse_date("aAௗ0㌀0"));
}
fn main() { test_unicode_gibberish(); }

Now, let’s see what happened… we forgot about UTF-8! You can’t just blindly slice strings since you could split a character, in this case that Tamil diacritic placed atop other characters in the string.

In the interest of making the code changes as small as possible, we’ll just check that the string is ASCII and reject anything that isn’t.

#![allow(unused)]
fn main() {
use std::ascii::AsciiExt;

fn parse_date(s: &str) -> Option<(u32, u32, u32)> {
    if 10 != s.len() { return None; }

    // NEW: Ignore non-ASCII strings so we don't need to deal with Unicode.
    if !s.is_ascii() { return None; }

    if "-" != &s[4..5] || "-" != &s[7..8] { return None; }

    let year = &s[0..4];
    let month = &s[6..7];
    let day = &s[8..10];

    year.parse::<u32>().ok().and_then(
        |y| month.parse::<u32>().ok().and_then(
            |m| day.parse::<u32>().ok().map(
                |d| (y, m, d))))
}
}

The tests pass now! But we know there are still more problems, so let’s test more properties.

Another property we want from our code is that it parses every valid date. We can add another test to the proptest! section:

extern crate proptest;
use proptest::prelude::*;
fn parse_date(s: &str) -> Option<(u32, u32, u32)> {
    if 10 != s.len() { return None; }

    // NEW: Ignore non-ASCII strings so we don't need to deal with Unicode.
    if !s.is_ascii() { return None; }

    if "-" != &s[4..5] || "-" != &s[7..8] { return None; }

    let year = &s[0..4];
    let month = &s[6..7];
    let day = &s[8..10];

    year.parse::<u32>().ok().and_then(
        |y| month.parse::<u32>().ok().and_then(
            |m| day.parse::<u32>().ok().map(
                |d| (y, m, d))))
}
proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn parses_all_valid_dates(s in "[0-9]{4}-[0-9]{2}-[0-9]{2}") {
        parse_date(&s).unwrap();
    }
}
fn main() { parses_all_valid_dates(); }

The thing to the right-hand side of in is actually a regular expression, and s is chosen from strings which match it. So in our previous test, "\\PC*" was generating arbitrary strings composed of arbitrary non-control characters. Now, we generate things in the YYYY-MM-DD format.

The new test passes, so let’s move on to something else.

The final property we want to check is that the dates are actually parsed correctly. Now, we can’t do this by generating strings — we’d end up just reimplementing the date parser in the test! Instead, we start from the expected output, generate the string, and check that it gets parsed back.

#![allow(unused)]
fn main() {
extern crate proptest;
use proptest::prelude::*;
fn parse_date(s: &str) -> Option<(u32, u32, u32)> {
    if 10 != s.len() { return None; }

    // NEW: Ignore non-ASCII strings so we don't need to deal with Unicode.
    if !s.is_ascii() { return None; }

    if "-" != &s[4..5] || "-" != &s[7..8] { return None; }

    let year = &s[0..4];
    let month = &s[6..7];
    let day = &s[8..10];

    year.parse::<u32>().ok().and_then(
        |y| month.parse::<u32>().ok().and_then(
            |m| day.parse::<u32>().ok().map(
                |d| (y, m, d))))
}
proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn parses_date_back_to_original(y in 0u32..10000,
                                    m in 1u32..13, d in 1u32..32) {
        let (y2, m2, d2) = parse_date(
            &format!("{:04}-{:02}-{:02}", y, m, d)).unwrap();
        // prop_assert_eq! is basically the same as assert_eq!, but doesn't
        // cause a bunch of panic messages to be printed on intermediate
        // test failures. Which one to use is largely a matter of taste.
        prop_assert_eq!((y, m, d), (y2, m2, d2));
    }
}
}

Here, we see that besides regexes, we can use any expression which is a proptest::strategy::Strategy, in this case, integer ranges.

The test fails when we run it. Though there’s not much output this time.

thread 'main' panicked at 'Test failed: assertion failed: `(left == right)` (left: `(0, 10, 1)`, right: `(0, 0, 1)`) at examples/dateparser_v2.rs:46; minimal failing input: y = 0, m = 10, d = 1
	successes: 2
	local rejects: 0
	global rejects: 0
', examples/dateparser_v2.rs:33
note: Run with `RUST_BACKTRACE=1` for a backtrace.

The failing input is (y, m, d) = (0, 10, 1), which is a rather specific output. Before thinking about why this breaks the code, let’s look at what proptest did to arrive at this value. At the start of our test function, insert

#![allow(unused)]
fn main() {
    let (y, m, d) = (0, 10, 1);
    println!("y = {}, m = {}, d = {}", y, m, d);
}

Running the test again, we get something like this:

y = 2497, m = 8, d = 27
y = 9641, m = 8, d = 18
y = 7360, m = 12, d = 20
y = 3680, m = 12, d = 20
y = 1840, m = 12, d = 20
y = 920, m = 12, d = 20
y = 460, m = 12, d = 20
y = 230, m = 12, d = 20
y = 115, m = 12, d = 20
y = 57, m = 12, d = 20
y = 28, m = 12, d = 20
y = 14, m = 12, d = 20
y = 7, m = 12, d = 20
y = 3, m = 12, d = 20
y = 1, m = 12, d = 20
y = 0, m = 12, d = 20
y = 0, m = 6, d = 20
y = 0, m = 9, d = 20
y = 0, m = 11, d = 20
y = 0, m = 10, d = 20
y = 0, m = 10, d = 10
y = 0, m = 10, d = 5
y = 0, m = 10, d = 3
y = 0, m = 10, d = 2
y = 0, m = 10, d = 1

The test failure message said there were two successful cases; we see these at the very top, 2497-08-27 and 9641-08-18. The next case, 7360-12-20, failed. There’s nothing immediately obviously special about this date. Fortunately, proptest reduced it to a much simpler case. First, it rapidly reduced the y input to 0 at the beginning, and similarly reduced the d input to the minimum allowable value of 1 at the end. Between those two, though, we see something different: it tried to shrink 12 to 6, but then ended up raising it back up to 10. This is because the 0000-06-20 and 0000-09-20 test cases passed.

In the end, we get the date 0000-10-01, which apparently gets parsed as 0000-00-01. Again, this failing case was added to the failure persistence file, and we should add this as its own unit test:

$ git add proptest-regressions

fn parse_date(s: &str) -> Option<(u32, u32, u32)> {
    if 10 != s.len() { return None; }

    // NEW: Ignore non-ASCII strings so we don't need to deal with Unicode.
    if !s.is_ascii() { return None; }

    if "-" != &s[4..5] || "-" != &s[7..8] { return None; }

    let year = &s[0..4];
    let month = &s[6..7];
    let day = &s[8..10];

    year.parse::<u32>().ok().and_then(
        |y| month.parse::<u32>().ok().and_then(
            |m| day.parse::<u32>().ok().map(
                |d| (y, m, d))))
}
#[test]
fn dummy() {} // Doctests don't build `#[test]` functions, so we need this
fn test_october_first() {
    assert_eq!(Some((0, 10, 1)), parse_date("0000-10-01"));
}
fn main() { test_october_first(); }

Now to figure out what’s broken in the code. Even without the intermediate input, we can say with reasonable confidence that the year and day parts don’t come into the picture since both were reduced to the minimum allowable input. The month input was not, but was reduced to 10. This means we can infer that there’s something special about 10 that doesn’t hold for 9. In this case, that “special something” is being two digits wide. In our code:

    let month = &s[6..7];

We were off by one, and need to use the range 5..7. After fixing this, the test passes.

The proptest! macro has some additional syntax, including for setting configuration for things like the number of test cases to generate. See its documentation for more details.

Proptest from the Bottom Up

This tutorial will introduce proptest from the bottom up, starting from the basic building blocks, in the hopes of making the model as a whole clear. In particular, we’ll start off without using the macros so that the macros can later be understood in terms of what they expand into rather than magic. But as a result, the first part is not representative of how proptest is normally used. If bottom-up isn’t your style, you may wish to skim the first few sections.

Also note that the examples here focus on the usage of proptest itself, and as such generally have trivial test bodies. In real code, you would obviously have assertions and so forth in the test bodies.

Strategy Basics

Please make sure to read the introduction to this tutorial before starting this section.

The Strategy is the most fundamental concept in proptest. A strategy defines two things:

• How to generate random values of a particular type from a random number generator.

• How to “shrink” such values into “simpler” forms.

Proptest ships with a substantial library of strategies. Some of these are defined in terms of built-in types; for example, 0..100i32 is a strategy to generate i32s between 0, inclusive, and 100, exclusive. As we’ve already seen, strings are themselves strategies for generating strings which match the former as a regular expression.

Generating a value is a two-step process. First, a TestRunner is passed to the new_tree() method of the Strategy; this returns a ValueTree, which we’ll look at in more detail momentarily. Calling the current() method on the ValueTree produces the actual value. Knowing that, we can put the pieces together and generate values. The below is the tutorial-strategy-play.rs example:

extern crate proptest;
use proptest::test_runner::TestRunner;
use proptest::strategy::{Strategy, ValueTree};

fn main() {
    let mut runner = TestRunner::default();
    let int_val = (0..100i32).new_tree(&mut runner).unwrap();
    let str_val = "[a-z]{1,4}\\p{Cyrillic}{1,4}\\p{Greek}{1,4}"
        .new_tree(&mut runner).unwrap();
    println!("int_val = {}, str_val = {}",
             int_val.current(), str_val.current());
}

If you run this a few times, you’ll get output similar to the following:

$ target/debug/examples/tutorial-strategy-play
int_val = 99, str_val = vѨͿἕΌ
$ target/debug/examples/tutorial-strategy-play
int_val = 25, str_val = cwᵸійΉ
$ target/debug/examples/tutorial-strategy-play
int_val = 5, str_val = oegiᴫᵸӈᵸὛΉ

This knowledge is sufficient to build an extremely primitive fuzzing test.

#![allow(unused)]
fn main() {
extern crate proptest;
use proptest::test_runner::TestRunner;
use proptest::strategy::{Strategy, ValueTree};

fn some_function(v: i32) {
    // Do a bunch of stuff, but crash if v > 500
    assert!(v <= 500);
}

#[test]
fn some_function_doesnt_crash() {
    let mut runner = TestRunner::default();
    for _ in 0..256 {
        let val = (0..10000i32).new_tree(&mut runner).unwrap();
        some_function(val.current());
    }
}
}

This works, but when the test fails, we don’t get much context, and even if we recover the input, we see some arbitrary-looking value like 1771 rather than the boundary condition of 501. For a function taking just an integer, this is probably still good enough, but as inputs get more complex, interpreting completely random values becomes increasingly difficult.

Shrinking Basics

Finding the “simplest” input that causes a test failure is referred to as shrinking. This is where the intermediate ValueTree type comes in. Besides current(), it provides two methods — simplify() and complicate() — which together allow binary searching over the input space. The tutorial-simplify-play.rs example shows how repeated calls to simplify() produce incrementally “simpler” outputs, both in terms of size and in characters used.

extern crate proptest;
use proptest::test_runner::TestRunner;
use proptest::strategy::{Strategy, ValueTree};

fn main() {
    let mut runner = TestRunner::default();
    let mut str_val = "[a-z]{1,4}\\p{Cyrillic}{1,4}\\p{Greek}{1,4}"
        .new_tree(&mut runner).unwrap();
    println!("str_val = {}", str_val.current());
    while str_val.simplify() {
        println!("        = {}", str_val.current());
    }
}

A couple runs:

$ target/debug/examples/tutorial-simplify-play
str_val = vy꙲ꙈᴫѱΆῨῨ
        = y꙲ꙈᴫѱΆῨῨ
        = y꙲ꙈᴫѱΆῨῨ
        = m꙲ꙈᴫѱΆῨῨ
        = g꙲ꙈᴫѱΆῨῨ
        = d꙲ꙈᴫѱΆῨῨ
        = b꙲ꙈᴫѱΆῨῨ
        = a꙲ꙈᴫѱΆῨῨ
        = aꙈᴫѱΆῨῨ
        = aᴫѱΆῨῨ
        = aѱΆῨῨ
        = aѱΆῨῨ
        = aѱΆῨῨ
        = aиΆῨῨ
        = aМΆῨῨ
        = aЎΆῨῨ
        = aЇΆῨῨ
        = aЃΆῨῨ
        = aЁΆῨῨ
        = aЀΆῨῨ
        = aЀῨῨ
        = aЀῨ
        = aЀῨ
        = aЀῢ
        = aЀ῟
        = aЀ῞
        = aЀ῝
$ target/debug/examples/tutorial-simplify-play
str_val = dyiꙭᾪῇΊ
        = yiꙭᾪῇΊ
        = iꙭᾪῇΊ
        = iꙭᾪῇΊ
        = iꙭᾪῇΊ
        = eꙭᾪῇΊ
        = cꙭᾪῇΊ
        = bꙭᾪῇΊ
        = aꙭᾪῇΊ
        = aꙖᾪῇΊ
        = aꙋᾪῇΊ
        = aꙅᾪῇΊ
        = aꙂᾪῇΊ
        = aꙁᾪῇΊ
        = aꙀᾪῇΊ
        = aꙀῇΊ
        = aꙀΊ
        = aꙀΊ
        = aꙀΊ
        = aꙀΉ
        = aꙀΈ

Note that shrinking never shrinks a value to something outside the range the strategy describes. Notice the strings in the above example still match the regular expression even in the end. An integer drawn from 100..1000i32 will shrink towards zero, but will stop at 100 since that is the minimum value.

simplify() and complicate() can be used to adapt our primitive fuzz test to actually find the boundary condition.

extern crate proptest;
use proptest::test_runner::TestRunner;
use proptest::strategy::{Strategy, ValueTree};

fn some_function(v: i32) -> bool {
    // Do a bunch of stuff, but crash if v > 500
    // assert!(v <= 500);
    // But return a boolean instead of panicking for simplicity
    v <= 500
}

// We know the function is broken, so use a purpose-built main function to
// find the breaking point.
fn main() {
    let mut runner = TestRunner::default();
    for _ in 0..256 {
        let mut val = (0..10000i32).new_tree(&mut runner).unwrap();
        if some_function(val.current()) {
            // Test case passed
            continue;
        }

        // We found our failing test case, simplify it as much as possible.
        loop {
            if !some_function(val.current()) {
                // Still failing, find a simpler case
                if !val.simplify() {
                    // No more simplification possible; we're done
                    break;
                }
            } else {
                // Passed this input, back up a bit
                if !val.complicate() {
                    break;
                }
            }
        }

        println!("The minimal failing case is {}", val.current());
        assert_eq!(501, val.current());
        return;
    }
    panic!("Didn't find a failing test case");
}

This code reliably finds the boundary of the failure, 501.

Using the Test Runner

Rather than manually shrinking, proptest’s TestRunner provides this functionality for us and additionally handles things like panics. The method we’re interested in is run. We simply give it the strategy and a function to test inputs and it takes care of the rest.

extern crate proptest;
use proptest::test_runner::{Config, FileFailurePersistence,
                            TestError, TestRunner};

fn some_function(v: i32) {
    // Do a bunch of stuff, but crash if v > 500.
    // We return to normal `assert!` here since `TestRunner` catches
    // panics.
    assert!(v <= 500);
}

// We know the function is broken, so use a purpose-built main function to
// find the breaking point.
fn main() {
    let mut runner = TestRunner::new(Config {
        // Turn failure persistence off for demonstration
        failure_persistence: Some(Box::new(FileFailurePersistence::Off)),
        .. Config::default()
    });
    let result = runner.run(&(0..10000i32), |v| {
        some_function(v);
        Ok(())
    });
    match result {
        Err(TestError::Fail(_, value)) => {
            println!("Found minimal failing case: {}", value);
            assert_eq!(501, value);
        },
        result => panic!("Unexpected result: {:?}", result),
    }
}

That’s a lot better! Still a bit boilerplatey; the proptest! macro will help with that, but it does some other stuff we haven’t covered yet, so for the moment we’ll keep using TestRunner directly.

Compound Strategies

Testing functions that take single arguments of primitive types is nice and all, but is kind of underwhelming. Back when we were writing the whole stack by hand, extending the technique to, say, two integers was clear, if verbose. But TestRunner only takes a single Strategy; how can we test a function that needs inputs from more than one?

extern crate proptest;
use proptest::test_runner::TestRunner;

fn add(a: i32, b: i32) -> i32 {
    a + b
}

#[test]
fn dummy() {} // Doctests don't build `#[test]` functions, so we need this
fn test_add() {
    let mut runner = TestRunner::default();
    runner.run(/* uhhm... */).unwrap();
}
fn main() { test_add(); }

The key is that strategies are composable. The simplest form of composition is “compound strategies”, where we take multiple strategies and combine their values into one value that holds each input separately. There are several of these. The simplest is a tuple; a tuple of strategies is itself a strategy for tuples of the values those strategies produce. For example, (0..100i32,100..1000i32) is a strategy for pairs of integers where the first value is between 0 and 100 and the second is between 100 and 1000.

So for our two-argument function, our strategy is simply a tuple of ranges.

extern crate proptest;
use proptest::test_runner::TestRunner;

fn add(a: i32, b: i32) -> i32 {
    a + b
}

#[test]
fn dummy() {} // Doctests don't build `#[test]` functions, so we need this
fn test_add() {
    let mut runner = TestRunner::default();
    // Combine our two inputs into a strategy for one tuple. Our test
    // function then destructures the generated tuples back into separate
    // `a` and `b` variables to be passed in to `add()`.
    runner.run(&(0..1000i32, 0..1000i32), |(a, b)| {
        let sum = add(a, b);
        assert!(sum >= a);
        assert!(sum >= b);
        Ok(())
    }).unwrap();
}
fn main() { test_add(); }

Other compound strategies include fixed-sizes arrays of strategies and Vecs of strategies (which produce arrays or Vecs of values parallel to the strategy collection), as well as the various strategies provided in the collection module.

Syntax Sugar: proptest!

Now that we know about compound strategies, we can understand how the proptest! macro works. Our example from the prior section can be rewritten using that macro like so:

extern crate proptest;
use proptest::prelude::*;

fn add(a: i32, b: i32) -> i32 {
    a + b
}

proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn test_add(a in 0..1000i32, b in 0..1000i32) {
        let sum = add(a, b);
        assert!(sum >= a);
        assert!(sum >= b);
    }
}

fn main() { test_add(); }

Conceptually, the desugaring process is fairly simple. At the start of the test function, a new TestRunner is constructed. The input strategies (after the in keyword) are grouped into a tuple. That tuple is passed in to the TestRunner as the input strategy. The test body has Ok(()) added to the end, then is put into a lambda that destructures the generated input tuple back into the named parameters and then runs the body. The end result is extremely similar to what we wrote by hand in the prior section.

proptest! actually does a few other things in order to make failure output easier to read and to overcome the 10-tuple limit.

Transforming Strategies

Suppose you have a function that takes a string which needs to be the Display format of an arbitrary u32. A first attempt to providing this argument might be to use a regular expression, like so:

extern crate proptest;
use proptest::prelude::*;

fn do_stuff(v: String) {
    let i: u32 = v.parse().unwrap();
    let s = i.to_string();
    assert_eq!(s, v);
}

proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn test_do_stuff(v in "[1-9][0-9]{0,8}") {
        do_stuff(v);
    }
}
fn main() { test_do_stuff(); }

This kind of works, but it has problems. For one, it does not explore the whole u32 space. It is possible to write a regular expression that does, but such an expression is rather long, and also results in a pretty odd distribution of values. The input also doesn’t shrink correctly, since proptest tries to shrink it in terms of a string rather than an integer.

What you really want to do is generate a u32 and then pass in its string representation. One way to do this is to just take u32 as an input to the test and then transform it to a string within the test code. This approach works fine, but isn’t reusable or composable. Ideally, we could get a strategy that does this.

The thing we’re looking for is the first strategy combinator, prop_map. We need to ensure Strategy is in scope to use it.

extern crate proptest;
// Grab `Strategy`, shorter namespace prefix, and the macros
use proptest::prelude::*;

fn do_stuff(v: String) {
    let i: u32 = v.parse().unwrap();
    let s = i.to_string();
    assert_eq!(s, v);
}

proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn test_do_stuff(v in any::<u32>().prop_map(|v| v.to_string())) {
        do_stuff(v);
    }
}
fn main() { test_do_stuff(); }

Calling prop_map on a Strategy creates a new strategy which transforms every generated value using the provided function. Proptest retains the relationship between the original Strategy and the transformed one; as a result, shrinking occurs in terms of u32, even though we’re generating a String.

prop_map is also the principal way to define strategies for new types, since most types are simply composed of other, simpler values.

Let’s update our code so it takes a more interesting structure.

extern crate proptest;
use proptest::prelude::*;

#[derive(Clone, Debug)]
struct Order {
  id: String,
  // Some other fields, though the test doesn't do anything with them
  item: String,
  quantity: u32,
}

fn do_stuff(order: Order) {
    let i: u32 = order.id.parse().unwrap();
    let s = i.to_string();
    assert_eq!(s, order.id);
}

proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn test_do_stuff(
        order in
        (any::<u32>().prop_map(|v| v.to_string()),
         "[a-z]*", 1..1000u32).prop_map(
             |(id, item, quantity)| Order { id, item, quantity })
    ) {
        do_stuff(order);
    }
}
fn main() { test_do_stuff(); }

Notice how we were able to take the output from prop_map and put it in a tuple, then call prop_map on that tuple to produce yet another value.

But that’s quite a mouthful in the argument list. Fortunately, strategies are normal values, so we can extract it to a function.

extern crate proptest;
use proptest::prelude::*;

// snip

#[derive(Clone, Debug)]
struct Order {
  id: String,
  // Some other fields, though the test doesn't do anything with them
  item: String,
  quantity: u32,
}

fn do_stuff(order: Order) {
    let i: u32 = order.id.parse().unwrap();
    let s = i.to_string();
    assert_eq!(s, order.id);
}

fn arb_order(max_quantity: u32) -> BoxedStrategy<Order> {
    (any::<u32>().prop_map(|v| v.to_string()),
     "[a-z]*", 1..max_quantity)
    .prop_map(|(id, item, quantity)| Order { id, item, quantity })
    .boxed()
}

proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn test_do_stuff(order in arb_order(1000)) {
        do_stuff(order);
    }
}
fn main() { test_do_stuff(); }

We boxed() the strategy in the function since otherwise the type would not be nameable, and even if it were, it would be very hard to read or write. Boxing a Strategy turns both it and its ValueTrees into trait objects, which both makes the types simpler and can be used to mix heterogeneous Strategy types as long as they produce the same value types.

The arb_order() function is also parameterised, which is another advantage of extracting strategies to separate functions. In this case, if we have a test that needs an Order with no more than a dozen items, we can simply call arb_order(12) rather than needing to write out a whole new strategy.

We can also use -> impl Strategy<Value = Order> instead to avoid the overhead as in the following example. You should use -> impl Strategy<..> unless you need the dynamic dispatch.

extern crate proptest;
use proptest::prelude::*;

// snip

#[derive(Clone, Debug)]
struct Order {
  id: String,
  // Some other fields, though the test doesn't do anything with them
  item: String,
  quantity: u32,
}

fn do_stuff(order: Order) {
    let i: u32 = order.id.parse().unwrap();
    let s = i.to_string();
    assert_eq!(s, order.id);
}

fn arb_order(max_quantity: u32) -> impl Strategy<Value = Order> {
    (any::<u32>().prop_map(|v| v.to_string()),
     "[a-z]*", 1..max_quantity)
    .prop_map(|(id, item, quantity)| Order { id, item, quantity })
}

proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn test_do_stuff(order in arb_order(1000)) {
        do_stuff(order);
    }
}

fn main() { test_do_stuff(); }

Syntax Sugar: prop_compose!

Defining strategy-returning functions like this is extremely useful, but the code above is a bit verbose, as well as hard to read for similar reasons to writing test functions by hand.

To simplify this task, proptest includes the prop_compose! macro. Before going into details, here’s our code from above rewritten to use it.

extern crate proptest;
use proptest::prelude::*;

// snip

#[derive(Clone, Debug)]
struct Order {
  id: String,
  // Some other fields, though the test doesn't do anything with them
  item: String,
  quantity: u32,
}

fn do_stuff(order: Order) {
    let i: u32 = order.id.parse().unwrap();
    let s = i.to_string();
    assert_eq!(s, order.id);
}
prop_compose! {
    fn arb_order_id()(id in any::<u32>()) -> String {
        id.to_string()
    }
}
prop_compose! {
    fn arb_order(max_quantity: u32)
                (id in arb_order_id(), item in "[a-z]*",
                 quantity in 1..max_quantity)
                -> Order {
        Order { id, item, quantity }
    }
}

proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn test_do_stuff(order in arb_order(1000)) {
        do_stuff(order);
    }
}
fn main() { test_do_stuff(); }

We had to extract arb_order_id() out into its own function, but otherwise this desugars to almost exactly what we wrote in the previous section. The generated function takes the first parameter list as arguments. These arguments are used to select the strategies in the second argument list. Values are then drawn from those strategies and transformed by the function body. The actual function has a return type of impl Strategy<Value = T> where T is the declared return type.

Generating Enums

The syntax sugar for defining strategies for enums is currently somewhat limited. Creating such strategies with prop_compose! is possible but generally is not very readable, so in most cases defining the function by hand is preferable.

The core building block is the prop_oneof! macro, in which you list one case for each case in your enum. For enums which have no data, the strategy for each case is Just(YourEnum::TheCase). Enum cases with data generally require putting the data in a tuple and then using prop_map to map it into the enum case.

Here is a simple example:

#![allow(unused)]
fn main() {
extern crate proptest;
use proptest::prelude::*;

#[derive(Debug, Clone)]
enum MyEnum {
    SimpleCase,
    CaseWithSingleDatum(u32),
    CaseWithMultipleData(u32, String),
}

fn my_enum_strategy() -> impl Strategy<Value = MyEnum> {
  prop_oneof![
    // For cases without data, `Just` is all you need
    Just(MyEnum::SimpleCase),

    // For cases with data, write a strategy for the interior data, then
    // map into the actual enum case.
    any::<u32>().prop_map(MyEnum::CaseWithSingleDatum),

    (any::<u32>(), ".*").prop_map(
      |(a, b)| MyEnum::CaseWithMultipleData(a, b)),
  ]
}
}

In general, it is best to list the enum cases in order from “simplest” to “most complex”, since shrinking will shrink down toward items earlier in the list.

For particularly complex enum cases, it can be helpful to extract the strategy for that case to a separate strategy. Here, prop_compose! can be of use.

#![allow(unused)]
fn main() {
extern crate proptest;
use proptest::prelude::*;

#[derive(Debug, Clone)]
enum MyComplexEnum {
    SimpleCase,
    AnotherSimpleCase,
    ComplexCase {
        product_code: String,
        id: u64,
        chapter: String,
    },
}

prop_compose! {
  fn my_complex_enum_complex_case()(
      product_code in "[0-9A-Z]{10,20}",
      id in 1u64..10000u64,
      chapter in "X{0,2}(V?I{1,3}|IV|IX)",
  ) -> MyComplexEnum {
      MyComplexEnum::ComplexCase { product_code, id, chapter }
  }
}

fn my_enum_strategy() -> BoxedStrategy<MyComplexEnum> {
  prop_oneof![
    Just(MyComplexEnum::SimpleCase),
    Just(MyComplexEnum::AnotherSimpleCase),
    my_complex_enum_complex_case(),
  ].boxed()
}
}

Filtering

Sometimes, you have a case where your input values have some sort of “irregular” constraint on them. For example, an integer needing to be even, or two values needing to be non-equal.

In general, the ideal solution is to find a way to take a seed value and then use prop_map to transform it into the desired, irregular domain. For example, to generate even integers, use something like

#![allow(unused)]
fn main() {
extern crate proptest;
use proptest::prelude::*;
prop_compose! {
    // Generate arbitrary integers up to half the maximum desired value,
    // then multiply them by 2, thus producing only even integers in the
    // desired range.
    fn even_integer(max: i32)(base in 0..max/2) -> i32 { base * 2 }
}
}

For the cases where this is not viable, it is possible to filter strategies. Proptest actually divides filters into two categories:

• “Local” filters apply to a single strategy. If a value is rejected, a new value is drawn from that strategy only.

• “Global” filters apply to the whole test case. If the test case is rejected, the whole thing is regenerated.

The distinction is somewhat arbitrary, since something like a “global filter” could be created by just putting a “local filter” around the whole input strategy. In practise, the distinction is as to what code performs the rejection.

A local filter is created with the prop_filter combinator. Besides a function indicating whether to accept the value, it also takes a value of type &'static str, String, .., which it uses to record where/why the rejection happened.

extern crate proptest;
use proptest::prelude::*;

proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn some_test(
      v in (0..1000u32)
        .prop_filter("Values must not divisible by 7 xor 11",
                     |v| !((0 == v % 7) ^ (0 == v % 11)))
    ) {
        assert_eq!(0 == v % 7, 0 == v % 11);
    }
}
fn main() { some_test(); }

Global filtering results when a test itself returns Err(TestCaseError::Reject). The prop_assume! macro provides an easy way to do this.

extern crate proptest;
use proptest::prelude::*;

fn frob(a: i32, b: i32) -> (i32, i32) {
    let d = (a - b).abs();
    (a / d, b / d)
}

proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn test_frob(a in -1000..1000, b in -1000..1000) {
        // Input illegal if a==b.
        // Equivalent to
        // if (a == b) { return Err(TestCaseError::Reject(...)); }
        prop_assume!(a != b);

        let (a2, b2) = frob(a, b);
        assert!(a2.abs() <= a.abs());
        assert!(b2.abs() <= b.abs());
    }
}
fn main() { test_frob(); }

While useful, filtering has a lot of disadvantages:

• Since it is simply rejection sampling, it will slow down generation of test cases since values need to be generated additional times to satisfy the filter. In the case where a filter always returns false, a test could theoretically never generate a result.

• Proptest tracks how many local and global rejections have happened, and aborts if they exceed a certain number. This prevents a test taking an extremely long time due to rejections, but means not all filters are viable in the default configuration. The limits for local and global rejections are different; by default, proptest allows a large number of local rejections but a fairly small number of global rejections, on the premise that the former are cheap but potentially common (having been built into the strategy) but the latter are expensive but rare (being an edge case in the particular test).

• Shrinking and filtering do not play well together. When shrinking, if a value winds up being rejected, there is no pass/fail information to continue shrinking properly. Instead, proptest treats such a rejection the same way it handles a shrink that results in a passing test: by backing away from simplification with a call to complicate(). Thus encountering a filter rejection during shrinking prevents shrinking from continuing to any simpler values, even if there are some that would be accepted by the filter.

Generating Recursive Data

Randomly generating recursive data structures is trickier than it sounds. For example, the below is a naïve attempt at generating a JSON AST by using recursion.

#![allow(unused)]
fn main() {
extern crate proptest;
use std::collections::HashMap;
use proptest::prelude::*;

#[derive(Clone, Debug)]
enum Json {
    Null,
    Bool(bool),
    Number(f64),
    String(String),
    Array(Vec<Json>),
    Map(HashMap<String, Json>),
}

fn arb_json() -> impl Strategy<Value = Json> {
    prop_oneof![
        Just(Json::Null),
        any::<bool>().prop_map(Json::Bool),
        any::<f64>().prop_map(Json::Number),
        ".*".prop_map(Json::String),
        prop::collection::vec(arb_json(), 0..10).prop_map(Json::Array),
        prop::collection::hash_map(
          ".*", arb_json(), 0..10).prop_map(Json::Map),
    ].boxed()
}
}

Upon closer consideration, this obviously can’t work because arb_json() recurses unconditionally.

A more sophisticated attempt is to define one strategy for each level of nesting up to some maximum. This doesn’t overflow the stack, but as defined here, even four levels of nesting will produce trees with thousands of nodes; by eight levels, we get to tens of millions.

Proptest provides a more reliable solution in the form of the prop_recursive combinator. To use this, we create a strategy for the non-recursive case, then give the combinator that strategy, some size parameters, and a function to transform a nested strategy into a recursive strategy.

#![allow(unused)]
fn main() {
extern crate proptest;
use std::collections::HashMap;
use proptest::prelude::*;

#[derive(Clone, Debug)]
enum Json {
    Null,
    Bool(bool),
    Number(f64),
    String(String),
    Array(Vec<Json>),
    Map(HashMap<String, Json>),
}

fn arb_json() -> impl Strategy<Value = Json> {
    let leaf = prop_oneof![
        Just(Json::Null),
        any::<bool>().prop_map(Json::Bool),
        any::<f64>().prop_map(Json::Number),
        ".*".prop_map(Json::String),
    ];
    leaf.prop_recursive(
      8, // 8 levels deep
      256, // Shoot for maximum size of 256 nodes
      10, // We put up to 10 items per collection
      |inner| prop_oneof![
          // Take the inner strategy and make the two recursive cases.
          prop::collection::vec(inner.clone(), 0..10)
              .prop_map(Json::Array),
          prop::collection::hash_map(".*", inner, 0..10)
              .prop_map(Json::Map),
      ])
}
}

Higher-Order Strategies

A higher-order strategy is a strategy which is generated by another strategy. That sounds kind of scary, so let’s consider an example first.

Say you have a function you want to test that takes a slice and an index into that slice. If we use a fixed size for the slice, it’s easy, but maybe we need to test with different slice sizes. We could try something with a filter:

#![allow(unused)]
fn main() {
extern crate proptest;
use proptest::prelude::*;
fn some_function(stuff: &[String], index: usize) { /* do stuff */ }

proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn test_some_function(
        stuff in prop::collection::vec(".*", 1..100),
        index in 0..100usize
    ) {
        prop_assume!(index < stuff.len());
        some_function(&stuff, index);
    }
}
}

This doesn’t work very well. First off, you get a lot of global rejections since index will be outside of stuff 50% of the time. But secondly, it will be rare to actually get a small stuff vector, since it would have to randomly choose a small index at the same time.

The solution is the prop_flat_map combinator. This is sort of like prop_map, except that the transform returns a strategy instead of a value. This is more easily understood by implementing our example:

extern crate proptest;
use proptest::prelude::*;

fn some_function(stuff: Vec<String>, index: usize) {
    let _ = &stuff[index];
    // Do stuff
}

fn vec_and_index() -> impl Strategy<Value = (Vec<String>, usize)> {
    prop::collection::vec(".*", 1..100)
        .prop_flat_map(|vec| {
            let len = vec.len();
            (Just(vec), 0..len)
        })
}

proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn test_some_function((vec, index) in vec_and_index()) {
        some_function(vec, index);
    }
}
fn main() { test_some_function(); }

In vec_and_index(), we make a strategy to produce an arbitrary vector. But then we derive a new strategy based on values produced by the first one. The new strategy produces the generated vector unchanged, but also adds a valid index into that vector, which we can do by picking the strategy for that index based on the size of the vector.

Even though the new strategy specifies the singleton Just(vec) strategy for the vector, proptest still understands the connection to the original strategy and will shrink vec as well. All the while, index continues to be a valid index into vec.

prop_compose! actually allows making second-order strategies like this by simply providing three argument lists instead of two. The below desugars to something much like what we wrote by hand above, except that the index and vector’s positions are internally reversed due to borrowing limitations.

#![allow(unused)]
fn main() {
extern crate proptest;
use proptest::prelude::*;
prop_compose! {
    fn vec_and_index()(vec in prop::collection::vec(".*", 1..100))
                    (index in 0..vec.len(), vec in Just(vec))
                    -> (Vec<String>, usize) {
       (vec, index)
   }
}
}

Defining a canonical Strategy for a type

We previously used the function any as in any::<u32>() to generate a strategy for all u32s. This function works with the trait Arbitrary, which QuickCheck users may be familiar with. In proptest, this trait is already implemented for most owned types in the standard library, but you can of course implement it for your own types.

In some cases, where it makes sense to define a canonical strategy, such as in the JSON AST example, it is a good idea to implement Arbitrary.

The experimental proptest-derive crate can be used to automate implementing Arbitrary in common cases.

Configuring the number of tests cases required

The default number of successful test cases that must execute for a test as a whole to pass is currently 256. If you are not satisfied with this and want to run more or fewer, there are a few ways to do this.

The first way is to set the environment-variable PROPTEST_CASES to a value that can be successfully parsed as a u32. The value you set to this variable is now the new default. (This only applies when the std feature of proptest is enabled, which it is by default.)

Another way is to use #![proptest_config(expr)] inside proptest! where expr : Config. To only change the number of test cases, you can simply write:

extern crate proptest;
use proptest::prelude::*;

fn add(a: i32, b: i32) -> i32 { a + b }

proptest! {
    // The next line modifies the number of tests.
    #![proptest_config(ProptestConfig::with_cases(1000))]
    #[test]
    fn dummy(a in 0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn test_add(a in 0..1000i32, b in 0..1000i32) {
        let sum = add(a, b);
        assert!(sum >= a);
        assert!(sum >= b);
    }
}
fn main() {
    test_add();
}

Through the same proptest_config mechanism you may fine-tune your configuration through the Config type. See its documentation for more information.

Failure Persistence

By default, when Proptest finds a failing test case, it persists that failing case in a file named after the source containing the failing test, but in a separate directory tree rooted at proptest-regressions. Later runs of tests will replay those test cases before generating novel cases. This ensures that the test will not fail on one run and then spuriously pass on the next, and also exposes similar tests to the same known-problematic input.

(If you do not have an obvious source directory, you may instead find files next to the source files, with a different extension.)

It is recommended to check these files in to your source control so that other test runners (e.g., collaborators or a CI system) also replay these cases.

Note that, by default, all tests in the same crate will share that one persistence file. If you have a very large number of tests, it may be desirable to separate them into smaller groups so the number of extra test cases that get run is reduced. This can be done by adjusting the failure_persistence flag on Config.

There are two ways this persistence could theoretically be done.

The immediately obvious option is to persist a representation of the value itself, for example by using Serde. While this has some advantages, particularly being resistant to changes like tweaking the input strategy, it also has a lot of problems. Most importantly, there is no way to determine whether any given value is actually within the domain of the strategy that produces it. Thus, some (likely extremely fragile) mechanism to ensure that the strategy that produced the value exactly matches the one in use in a test case would be required.

The other option is to store the seed that was used to produce the failing test case. This approach requires no support from the strategy or the produced value. If the strategy in use differs from the one used to produce failing case that was persisted, the seed may or may not produce the problematic value, but nonetheless produces a valid value. Due to these advantages, this is the approach Proptest uses.

Forking and Timeouts

By default, proptest tests are run in-process and are allowed to run for however long it takes them. This is resource-efficient and produces the nicest test output, and for many use cases is sufficient. However, problems like overflowing the stack, aborting the process, or getting stuck in an infinite loop will simply break the entire test process and prevent proptest from determining a minimal reproducible case.

As of version 0.7.1, proptest has optional “fork” and “timeout” features (both enabled by default), which make it possible to run your test cases in a subprocess and limit how long they may run. This is generally slower, may make using a debugger more difficult, and makes test output harder to interpret, but allows proptest to find and minimise test cases for these situations as well.

To use these features, simply set the fork and/or timeout fields on the Config. (Setting timeout implies fork.)

Here is a simple example of using both features:

extern crate proptest;
use proptest::prelude::*;

// The worst possible way to calculate Fibonacci numbers
fn fib(n: u64) -> u64 {
    if n <= 1 {
        n
    } else {
        fib(n - 1) + fib(n - 2)
    }
}

proptest! {
    #![proptest_config(ProptestConfig {
        // Setting both fork and timeout is redundant since timeout implies
        // fork, but both are shown for clarity.
        fork: true,
        timeout: 100,
        cases: 1, // Need to set this to 1 to avoid doctest running forever
        .. ProptestConfig::default()
    })]
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn test_fib(n: u64) {
        // For large n, this will variously run for an extremely long time,
        // overflow the stack, or panic due to integer overflow.
        assert!(fib(n) >= n);
    }
}
fn main() { test_fib(); }

The exact value of the test failure depends heavily on the performance of the host system, the rust version, and compiler flags, but on the system where it was originally tested, it found that the maximum value that fib() could handle was 39, despite having dozens of processes dump core due to stack overflow or time out along the way.

If you just want to run tests in subprocesses or with a timeout every now and then, you can do that by setting the PROPTEST_FORK or PROPTEST_TIMEOUT environment variables to alter the default configuration. For example, on Unix,

# Run all the proptest tests in subprocesses with no timeout.
# Individual tests can still opt out by setting `fork: false` in their
# own configuration.
PROPTEST_FORK=true cargo test
# Run all the proptest tests in subprocesses with a 1 second timeout.
# Tests can still opt out or use a different timeout by setting `timeout: 0`
# or another timeout in their own configuration.
PROPTEST_TIMEOUT=1000 cargo test

no_std Support

Proptest has partial support for being used in no_std contexts.

You will need a nightly compiler version. In your Cargo.toml, adjust the Proptest dependency to look something like this:

[dev-dependencies.proptest]
version = "proptestVersion"

# Opt out of the `std` feature
default-features = false

# alloc: Use the `alloc` crate directly. Proptest has a hard requirement on
# memory allocation, so either this or `std` is needed.
# unstable: Enable use of nightly-only compiler features.
features = ["alloc", "unstable"]

Some APIs are not available in the no_std build. This includes functionality which necessarily needs std such as failure persistence and forking, as well as features depending on other crates which do not support no_std usage, such as regex support.

The no_std build may not have access to an entropy source (one exception are x86-64 machines that support rdrand, in this case the library can be compiled with the hardware-rng feature to get random numbers). If no entropy source is available, every TestRunner (i.e., every #[test] when using the proptest! macro) uses a single hard-coded seed. For complex inputs, it may be a good idea to increase the number of test cases to compensate. The hard-coded seed is not contractually guaranteed and may change between Proptest releases without notice.

Web Assembly support

As of 0.9.2, it is possible to compile proptest on wasm targets. Please note that this is highly experimental and has not been subject to any substantial amount of testing.

In cargo.toml, write something like

[dev-dependencies.proptest]
version = "$proptestVersion"
# The default feature set includes things like process forking which are not
# supported in Web Assembly.
default-features = false
# Enable using the `std` crate.
features = ["std"]

A few APIs are unavailable on wasm targets (beyond those which are removed by deselecting certain default features):

• Numeric strategies for i128 and u128.

• The Arbitrary implementation for std::env::VarError.

Limitations of Property Testing

Given infinite time, property testing will eventually explore the whole input space to a test. However, time is not infinite, so only a randomly sampled portion of the input space can be explored. This means that property testing is extremely unlikely to find single-value edge cases in a large space. For example, the following test will virtually always pass:

extern crate proptest;
use proptest::prelude::*;

proptest! {
    #[test]
    fn dummy(0..1) {} // Doctests don't build `#[test]` functions, so we need this
    fn i64_abs_is_never_negative(a: i64) {
        // This actually fails if a == i64::MIN, but randomly picking one
        // specific value out of 2⁶⁴ is overwhelmingly unlikely.
        assert!(a.abs() >= 0);
    }
}
fn main() { i64_abs_is_never_negative() }

Because of this, traditional unit testing with intelligently selected cases is still necessary for many kinds of problems.

Similarly, in some cases it can be hard or impossible to define a strategy which actually produces useful inputs. A strategy of .{1,4096} may be great to fuzz a C parser, but is highly unlikely to produce anything that makes it to a code generator.

Differences between QuickCheck and Proptest

QuickCheck and Proptest are similar in many ways: both generate random inputs for a function to check certain properties, and automatically shrink inputs to minimal failing cases.

The one big difference is that QuickCheck generates and shrinks values based on type alone, whereas Proptest uses explicit Strategy objects. The QuickCheck approach has a lot of disadvantages in comparison:

• QuickCheck can only define one generator and shrinker per type. If you need a custom generation strategy, you need to wrap it in a newtype and implement traits on that by hand. In Proptest, you can define arbitrarily many different strategies for the same type, and there are plenty built-in.

• For the same reason, QuickCheck has a single “size” configuration that tries to define the range of values generated. If you need an integer between 0 and 100 and another between 0 and 1000, you probably need to do another newtype. In Proptest, you can directly just express that you want a 0..100 integer and a 0..1000 integer.

• Types in QuickCheck are not easily composable. Defining Arbitrary and Shrink for a new struct which is simply produced by the composition of its fields requires implementing both by hand, including a bidirectional mapping between the struct and a tuple of its fields. In Proptest, you can make a tuple of the desired components and then prop_map it into the desired form. Shrinking happens automatically in terms of the input types.

• Because constraints on values cannot be expressed in QuickCheck, generation and shrinking may lead to a lot of input rejections. Strategies in Proptest are aware of simple constraints and do not generate or shrink to values that violate them.

The author of Hypothesis also has an article on this topic.

Of course, there’s also some relative downsides that fall out of what Proptest does differently:

• Generating complex values in Proptest can be up to an order of magnitude slower than in QuickCheck. This is because QuickCheck performs stateless shrinking based on the output value, whereas Proptest must hold on to all the intermediate states and relationships in order for its richer shrinking model to work.

Reference documentation

For the API reference documentation, please see the rustdoc documentation for the proptest crate.

State Machine testing

The state machine testing support is available in the proptest-state-machine crate.

When to use State Machine testing?

State machine testing automates the checking of properties of a system under test (SUT) against an abstract reference state machine definition. It does this by trying to discover a counter-example that breaks the defined properties of the system and attempts to shrink it to a minimal sequence of transitions that still reproduce the issue.

State machines are a very useful abstraction for reasoning about code. Many things from low-level to high-level logic and anywhere in between can be modelled as a state machine. They are very effective for modelling effectful code, that is code that performs some state changes that can be too hard to test thoroughly with a more manual approach or too complex to verify formally.

Some fitting examples to give you an idea include (by no means exhaustive):

• A data structure with an API that mutates its state
• An API for a database
• Interactions between a client(s) and a server

There is some initial investment needed to set the test up and it usually takes a bit more time to run than simple prop tests, but if correctness is important for your use case, you’ll be rewarded with a test that is so effective at discovering bugs it might feel almost magical, but as you’ll see, you could have easily implemented it yourself. Also, once you have the test setup, it is much easier to extend it and add new properties to check.

How to use it

Before using state machine testing, it is recommended to be at least familiar with the basic concepts of Proptest itself as it’s built on its essential foundations. That is:

• Strategies are composed from common proptest constructs and used to generate inputs to a state machine test.
• Because the generated transitions sequence is a strategy itself, a test will attempt to shrink them on a discovery of a case that breaks some properties.
• It will capture regressions file with a seed that can be used to deterministically repeat the found case.

In short, use ReferenceStateMachine and StateMachineTest to implement your state machine test and prop_state_machine! macro to run it.

If you just want to get started quickly, take a look at one of the examples:

• state_machine_heap.rs - a simple model to test an API of a heap data structure
• state_machine_echo_server.rs - a more advanced model for an echo server with multiple clients talking to it

To see what transitions are being applied in standard output as the state machine test executes, run these with e.g. PROPTEST_VERBOSE=1 cargo run --example state_machine_heap.

State machine testing is made up of two parts, an abstract reference state machine definition that drives the inputs to a test and a test definition for a SUT that replicates the same transitions as the reference state machine to find any possible divergence or conditions under which the defined properties (in here post-conditions and invariants) start to break.

Reference state machine strategy

You can get started with state machine testing by implementing trait ReferenceStateMachine, which is used to drive the generation of a sequence of transitions and can also be compared against the state of the SUT. At the minimum, this trait requires two associated types:

• type State that represents the state of the reference state machine.
• type Transition with possible transitions of the state machine. This is typically an enum with its variants containing input parameters for the transitions, if any.

You also have to implement three associated functions:

• To initialize the reference state machine:

  fn init_state() -> BoxedStrategy<Self::State>

  You can generate some random state with a strategy or use Just strategy for a constant value. Note that you can make a BoxedStrategy from any Strategy by simply calling .boxed() on it.

• To generate transitions:

  fn transitions(state: &Self::State) -> BoxedStrategy<Self::Transition>

  Most of the time, you’ll use prop_oneof! here. If a transition takes some input parameters, you can generate those with a Strategy and .prop_map it to the Transition variant. In more complex state machines, the set of valid transitions may depend on the current state. To that end, you can use the state argument, possibly combined with prop::sample::select function that allows you to create a strategy that selects a random value from an array or an array-like collection (be careful not to call select on an empty array as that will make it fail in a somewhat obscure way). For example, if you want to remove one of the existing keys from a hash map, you can select one of the keys from the current state and map it into a transition. Note that when you do something like this, you’ll also need to override the fn preconditions, which are explained in more detail below.

• To apply the given transition on the reference state:

  fn apply(mut state: Self::State, transition: &Self::Transition) -> Self::State

Additionally, you may want to override the default implementation of:

fn preconditions(state: &Self::State, transition: &Self::Transition) -> bool

By default, this simply returns true, which implies that there are no pre-conditions. Pre-conditions are a way of restricting what transitions are valid for a given state and you’ll only need to restrict the transitions whose validity depends on the current state. This ensures that the reference state machine will only produce and shrink to a sequence of valid transitions. It may not be immediately apparent that the current state may be affected by shrinking. With the example of selecting of keys of a hash map for fn transitions, you’ll need to check that the transition’s key is still present in the hash map, which may no longer be true after some shrinking is applied.

You can either implement ReferenceStateMachine for:

• A data structure that will represent your reference state machine and set the associated type State = Self; or
• An empty struct, which may be more convenient than making a wrapper type if you’re using a foreign type for the type State

Definition of a state machine test

With that out of the way, you can go ahead and implement StateMachineTest. This also requires two associated types:

• type SystemUnderTest which is the type that represents the SUT.
• type Reference with the type for which you implemented the ReferenceStateMachine.

There are also three associated functions to be implemented here (some types are slightly simplified for clarity):

• Initialize the SUT state:

  fn init_test(ref_state: &Self::Reference::State) -> Self::SystemUnderTest

  If your ReferenceStateMachine::init_state uses a non-constant strategy, you have to use the ref_state to initialize this to a corresponding state to ensure that you have consistent initial states.

• Apply the transition on the SUT state:

  fn apply(
    mut state: Self::SystemUnderTest,
    ref_state: &Self::Reference::State,
    transition: Transition
  ) -> Self::SystemUnderTest

  This is also where you’ll want to check any post-conditions that apply to a given transition, so after you apply the transition to the state, you can assert! some properties. Alternatively or additionally, you can use the ref_state for comparison, which will have the same transition that is given to this function already applied to it.

• Check properties that apply in any state:

  fn check_invariants(state: &Self::SystemUnderTest, ref_state: &Self::Reference::State)

  These must always hold and will be checked after every transition. Just like with apply, you have the option to use the ref_state for comparison.

To add some teardown logic to run at the end of each test case, you can override the teardown function, which by default simply drops the state:

fn teardown(state: Self::SystemUnderTest)

Make the state machine test runnable

Finally, to run the StateMachineTest, you can use the prop_state_machine! macro. For example:

prop_state_machine! {
  #[test]
  fn name_of_the_test(sequential 1..20 => MyStateMachineTest);
}

You pick a name_of_the_test and a single numerical value or a range after the sequential keyword for a number of transitions to be generated for the state machine execution. The MyStateMachineTest is whatever you’ve implemented the StateMachineTest for.

And that’s it. You can run the test, perhaps with cargo watch as you develop it further, and see if it can find some interesting counter-examples to your properties.

Extra tips

Because a state machine test may be heavier than regular prop tests, if you’re running your tests in a CI you may want to override the default proptest_config’s cases to include more or fewer cases in a single run. You can also use PROPTEST_CASES environment variable and during development it is preferable to override this to run many cases to get a better chance of catching those pesky bugs, erm, defects.

Given that there are thought to be in the region of another four million species that we have not yet even named, there is no doubt that scientists will be kept happily occupied studying them for millennia, so long as the insects remain to be studied. Would the world not be less rich, less surprising, less wonderful, if these peculiar creatures did not exist?

– Dave Goulson, Silent Earth

So let’s leave bugs alone and only squash defects instead!

Because the output of a failed test case can be a bit hard to read, it is often convenient to print the transitions. You can do that by simply setting the proptest_config’s verbose to 1 or higher. Again, if you don’t want to keep this in your test’s config or if you’d prefer to override the config, you could also use the PROPTEST_VERBOSE environment variable instead.

Another helpful config option that is good to know about is timeout (PROPTEST_TIMEOUT via an env var) for tests that may take longer to execute.

How does it work

This section goes into the inner workings of how the state machine is implemented, omitting some less interesting details. If you’re only interested in using it, you can consider this section an optional read.

The ReferenceStateMachine::sequential_strategy sets up a Sequential strategy that generates a sequence of transitions from the definition of the ReferenceStateMachine. The acceptability of each transition in the sequence depends on the current state of the state machine and ReferenceStateMachine::preconditions, if any. The state is updated by the transitions with the ReferenceStateMachine::apply function.

The Sequential strategy is then fed into Proptest like any other strategy via the prop_state_machine! macro and it produces a Vec<Transition> that gets passed into StateMachineTest::test_sequential where it is applied one by one to the SUT. Its post-conditions and invariants are checked during this process and if a failing case is found, the shrinking process kicks in until it can shrink no longer.

The shrinking strategy which is defined by the associated type Tree = SequentialValueTree of the Sequential strategy is to iteratively apply Shrink::InitialState, Shrink::DeleteTransition and Shrink::Transition (this can be found in proptest/src/strategy/state_machine.rs):

1. We start by trying to delete transitions from the back of the list until we can do so no further (the list has reached the min_size - that is the variable that gets set from the chosen range for the number of transitions in the prop_state_machine! invocation).
2. Then, we again iteratively attempt to shrink the individual transitions, but this time starting from the front of the list from the first transition to be applied.
3. Finally, we try to shrink the initial state until it’s not possible to shrink it any further.

The last applied shrink gets stored in the SequentialValueTree, so that if the shrinking process ends up in a case that no longer reproduces the discovered issue, the call to complicate in the ValueTree implementation of the SequentialValueTree can attempt to undo it.

Similar technologies

The state machine testing support for Proptest is heavily inspired by the Erlang’s eqc_statem (see the paper Finding Race Conditions in Erlang with QuickCheck and PULSE) with some key differences. Most notably:

• Currently, only sequential strategy is supported, but a concurrent strategy is planned to be added at later point.
• There are no “symbolic” variables like in eqc_statem. The state for the abstract (reference) state machine is separate from the state of the system under test.
• The post-conditions are not defined in their own function. Instead, they are part of the StateMachineTest::apply function.

Tips and Best Practices

Performance

Setting opt-level

Both the proptest crate and the random number generator it uses can be CPU intensive. If you are generating a lot of cases you may see a significant performance improvement by setting the opt-level to 3 in your Cargo.toml file:

[profile.test.package.proptest]
opt-level = 3

[profile.test.package.rand_chacha]
opt-level = 3

Reusing mutable resources

Sometimes you may want to reuse mutable resources across individual cases. For example, you may want to reuse a database connection or a file handle to avoid the overhead of opening and closing it for each case. Because the proptest! macro (when used with closure-style invocation) requires a Fn, you need to wrap your state in a RefCell:

#![allow(unused)]
fn main() {
extern crate proptest;
use std::cell::RefCell;
use proptest::proptest;

struct ConnectionPool {};
struct MyConnection {};
impl ConnectionPool {
   fn new() -> Self { Self {} }
   fn connect(&mut self) -> MyConnection { MyConnection {} }
}
#[test]
fn dummy() {}; // This is here to make the doctest work
fn test_with_shared_connection() {
    let mut my_conn = RefCell::new(ConnectionPool::new().connect());
    proptest!(|(x in 0..42)| {
        let mut conn = my_conn.borrow_mut();
        // Use state
    });
}
}

The proptest-derive crate

The proptest-derive crate provides a procedural macro, #[derive(Arbitrary)], which can be used to automatically generate simple Arbitrary implementations for user-defined types, allowing them to be used with any() and embedded in other #[derive(Arbitrary)] types without fuss.

It is recommended to have a basic working understanding of the proptest crate before getting into this part of the documentation.

This crate is currently somewhat experimental. Expect rough edges, particularly in documentation. It is also more likely to see releases with breaking changes than the main proptest crate.

Getting started

Cargo

To the [dev-dependencies] section of your Cargo.toml, add

proptest-derive = "0.2.0"

In a Rust 2015 crate, you must add

#[cfg(test)] extern crate proptest;

to the top of the crate.

About Versioning

proptest-derive is currently experimental and has its own version. Once it is more stable, it will be versioned in lock-step with the main proptest crate.

Using derive

Inside any of your test modules, you can simply add #[derive(Arbitrary)] to a struct or enum declaration.

#![allow(unused)]
fn main() {
#[cfg(test)]
mod test {
    use proptest::prelude::*;
    use proptest_derive::Arbitrary;

    #[derive(Arbitrary, Debug)]
    struct MyStruct {
        // ...
    }

    proptest! {
        #[test]
        fn test_one(my_struct: MyStruct) {
            // ...
        }

        // Equivalent to the above
        fn test_two(my_struct in any::<MyStruct>()) {
            // ...
        }
    }
}
}

In order to use proptest-derive on a type not in a test module without also depending on proptest for your main build, you must currently manually gate off the related annotations. This is something we plan to improve in the future.

#![allow(unused)]
fn main() {
#[cfg(test)] use proptest_derive::Arbitrary;

#[derive(Debug)]
// derive(Arbitrary) is only available in tests
#[cfg_attr(test, derive(Arbitrary))]
struct MyStruct {
    // Attributes consumed proptest-derive must not be added when the
    // declaration is not being processed by derive(Arbitrary).
    #[cfg_attr(test, proptest(value = 42))]
    answer: u32,
    // ...
}
}

Modifier Reference

All modifiers interpreted by #[derive(Arbitrary)] are of the form #[proptest(..)], where the content between the parentheses follows the normal Rust attribute syntax.

Each modifier within the parentheses is independent, in that putting two modifiers in the same attribute is equivalent to having two #[proptest(..)] attributes with one modifier each.

For brevity, modifiers are sometimes referenced by name alone; e.g., “the weight modifier” refers to #[proptest(weight = nn)] and not some freestanding #[weight] attribute.

filter

Form: #[proptest(filter = F)] or #[proptest(filter(F))] where F is either a bare identifier (i.e., naming a function) or a Rust expression in a string. In either case, the parameter must evaluate to something which is Fn (&T) -> bool, where T is the type of the item being filtered.

Usable on: structs, enums, enum variants, fields

The filter modifier allows filtering values generated for a field via rejection sampling. Since rejection sampling is inefficient and interferes with shrinking, it should only be used for conditions that are very rare or are unfeasible to express otherwise. In many cases, strategy can be used to more directly express the desired behaviour without rejection sampling. See the documentation for prop_filter for more details.

The argument to the modifier must be a valid argument for the second parameter of prop_filter.

Example:

#![allow(unused)]
fn main() {
extern crate proptest_derive;
extern crate proptest;
use proptest_derive::Arbitrary;
use proptest::prelude::*;

#[derive(Debug, Arbitrary)]
#[proptest(filter = "|segment| segment.start != segment.end")]
struct NonEmptySegment {
    start: i32,
    end: i32,
}
}

is equivalent to

#![allow(unused)]
fn main() {
extern crate proptest_derive;
extern crate proptest;
use proptest_derive::Arbitrary;
use proptest::prelude::*;

fn is_nonempty(segment: &NonEmptySegment) -> bool {
    segment.start != segment.end
}

#[derive(Debug, Arbitrary)]
#[proptest(filter = "is_nonempty")]
struct NonEmptySegment {
    start: i32,
    end: i32,
}
}

As mentioned above, filtering should be avoided when it is reasonably possible to express a non-filtering strategy that achieves the same effect. For example:

#![allow(unused)]
fn main() {
extern crate proptest_derive;
extern crate proptest;
use proptest_derive::Arbitrary;
use proptest::{proptest, arbitrary::any, strategy::Strategy};

#[derive(Debug, Arbitrary)]
struct BadExample {
    // Don't do this! Your tests will run more slowly and shrinking won't work
    // properly.
    #[proptest(filter = "|x| x % 2 == 0")]
    even_number: u32,
}

#[derive(Debug, Arbitrary)]
struct GoodExample {
    // Directly generate even numbers only by transforming the set of all
    // `u32`s and then mapping it to the set of even `u32`s.
    #[proptest(strategy = "any::<u32>().prop_map(|x| x / 2 * 2)")]
    even_number: u32,
}
}

no_bound

Form: #[proptest(no_bound)]

Usable on: generic type definitions and type parameters

Normally, when #[derive(Arbitrary)] is applied to an item with generic type parameter, every type parameter which is “used” (see below) is required to impl Arbitrary. For example, given a declaration like the following:

#![allow(unused)]
fn main() {
extern crate proptest_derive;
use proptest_derive::Arbitrary;

#[derive(Debug, Arbitrary)]
struct MyStruct<T> {
    t: T
    /* ... */
}
}

Something like this will be generated:

#![allow(unused)]
fn main() {
extern crate proptest;
use proptest::arbitrary::Arbitrary;

#[derive(Debug)]
struct MyStruct<T> {
t: T
}

impl<T> Arbitrary for MyStruct<T> where T: Arbitrary {
    type Parameters = u32;
    type Strategy = proptest::strategy::BoxedStrategy<Self>;
    fn arbitrary_with(_params: Self::Parameters) -> Self::Strategy { todo!() }
    /* ... */
}
}

Placing #[proptest(no_bound)] on a generic type definition is equivalent to placing the same attribute on every type parameter.

#![allow(unused)]
fn main() {
extern crate proptest_derive;
extern crate proptest;
use proptest_derive::Arbitrary;
use proptest::proptest;
use std::marker::PhantomData;

#[derive(Debug, Arbitrary)]
#[proptest(no_bound)]
struct MyStruct<A, B, C> {
    a: PhantomData<A>,
    b: PhantomData<B>,
    c: PhantomData<C>,
    /* ... */
}
}

This is equivalent to a hypothetical (but not currently supported) syntax like:

#![allow(unused)]
fn main() {
extern crate proptest_derive;
use proptest_derive::Arbitrary;
use std::marker::PhantomData;

#[derive(Debug, Arbitrary)]
struct MyStruct<
  #[proptest(no_bound)] A,
  #[proptest(no_bound)] B,
  #[proptest(no_bound)] C,
> {
    a: PhantomData<A>,
    b: PhantomData<B>,
    c: PhantomData<C>,
    /* ... */
}
}

A type parameter is “used” if the following hold:

• The enum or struct definition references it at least once, and that reference is not inside the type argument of a PhantomData.

• The item referencing the type parameter does not have any proptest modifiers which replace the usual use of Arbitrary, such as skip or value.

Due to the above, #[proptest(no_bound)] is generally only needed when the type parameter is used in another type which does not itself have an Arbitrary bound on the type.

no_params

Form: #[proptest(no_params)]

Usable on: structs, enums, enum variants, fields

On a struct or enum, no_params causes the Arbitrary parameter type to be (). All automatic delegations to Arbitrary on members of the item use Default::default() for their parameters.

On an enum variant or field, suppresses the addition of any parameter for the variant or field to the parameters for the whole struct. If the variant or field automatically delegates to Arbitrary for its value, that Arbitrary call uses Default::default() for its own parameter.

See the param modifier for more information on how parameters work.

params

Form: #[proptest(params = T)] or #[proptest(params(T))], where T is either a bare identifier or Rust code inside a string. In either case, the value must name a concrete Rust type which implements Default.

Usable on: structs, enums, enum variants, fields

The Arbitrary trait specifies a Parameters type which is used to control generation. By default, the Parameters type is a tuple of the parameters which are automatically passed to other Arbitrary implementations.

If applied to a struct or enum, params completely replaces the Parameters type. Any automatic delegations to other Arbitrary implementations then use Default::default() as there is no automatic way to locate an appropriate value (if there even is any) within the params type.

If applied to an enum variant or field, params specifies the parameters type for just that item, as if its type had an Arbitrary implementation taking that type. In this case, either value or strategy must be specified since the parameter type will not generally be compatible with the normal Arbitrary invocation (and in cases where it is, params would be useless if not used).

Any expressions (such as in the value and strategy modifiers) underneath an item with the params modifier has access to a variable named params which is of the type passed in #[proptest(params = ..)].

Examples:

#![allow(unused)]
fn main() {
extern crate proptest_derive;
extern crate proptest;
use proptest_derive::Arbitrary;
use proptest::prelude::*;

#[derive(Debug)]
struct WidgetRange(usize, usize);

impl Default for WidgetRange {
    fn default() -> Self { Self(0, 100) }
}

#[derive(Debug, Arbitrary)]
#[proptest(params(WidgetRange))]
struct WidgetCollection {
    #[proptest(strategy = "params.0 ..= params.1")]
    desired_widget_count: usize,
    // ...
}

// ...

proptest! {
    #[test]
    fn test_something(wc in any_with::<WidgetCollection>(WidgetRange(10, 20))) {
        assert!(wc.desired_widget_count >= 10 && wc.desired_widget_count <= 20);
    }
}
}

regex

Form: #[proptest(regex = "string")] or #[proptest(regex("string"))], where string is a regular expression. May also be invoked as #[proptest(regex(function_name))], where function_name is a no-argument function that returns an &'static str.

Usable on: fields

This modifier specifies to generate character or byte strings for a field which match a particular regular expression.

The regex modifier is equivalent to using the strategy modifier and enclosing the string in string_regex or bytes_regex. It can only be applied to fields of type String or Vec<u8>.

Example:

#![allow(unused)]
fn main() {
extern crate proptest_derive;
extern crate proptest;
use proptest_derive::Arbitrary;
use proptest::proptest;
#[derive(Debug, Arbitrary)]
struct FileContent {
    #[proptest(regex = "[a-z0-9.]+")]
    name: String,
    #[proptest(regex = "([0-9]+\n)*")]
    content: Vec<u8>,
}
}

skip

Form: #[proptest(skip)]

Usable on: enum variants

Annotating an enum variant with #[proptest(skip)] prevents proptest from generating that particular variant. This is useful when there is no sensible way to generate the variant or when you want to temporarily stop generating some variant during development.

Example:

#![allow(unused)]
fn main() {
extern crate proptest_derive;
extern crate proptest;
use proptest_derive::Arbitrary;
use proptest::prelude::*;

#[derive(Debug, Arbitrary)]
enum DataSource {
    Memory(Vec<u8>),

    // There's no way to produce an "arbitrary" file handle, so we skip
    // generating this case.
    #[proptest(skip)]
    File(std::fs::File),
}
}

It is an error to annotate all inhabited variants of an enum with #[proptest(skip)] as this leaves proptest with no options to generate the enum.

strategy

Form: #[proptest(strategy = S)] or #[proptest(strategy = S)], where S is either a string containing a Rust expression which evaluates to an appropriate Strategy, or a bare identifier naming a function which, when called with no arguments, returns such a Strategy.

Usable on: enum variants, fields

By default, enum variants are generated by recursing into their definition as is done for struct declarations, and fields are generated by invoking Arbitrary on the field type to produce a Strategy. The strategy modifier allows to manually provide a custom strategy directly.

In the case of fields, the strategy must produce values of the same type as that field. For enum variants, it must produce values of the enum type itself and these values ought to be of the variant in question.

Example:

#![allow(unused)]
fn main() {
extern crate proptest_derive;
extern crate proptest;
use proptest_derive::Arbitrary;
use proptest::prelude::*;
use proptest::strategy::Strategy;

#[derive(Debug, Arbitrary)]
enum Token {
    Delimitation {
        // This field is still generated via Arbitrary
        delimiter: Delimiter,

        // But for this field we use a custom strategy
        #[proptest(strategy = "1..(10 as u32)")]
        count: u32,

        // Here we also use a custom strategy, generated by the function
        // `offset_strategy`.
        #[proptest(strategy = "offset_strategy()")]
        offset: u32,
    },

    // Specify how to generate the whole enum variant
    #[proptest(strategy = "\"[a-zA-Z]+\".prop_map(Token::Word)")]
    Word(String),
}

#[derive(Debug, Arbitrary)]
enum Delimiter {
    Nope
    /* ... */
 }

fn offset_strategy() -> impl Strategy<Value = u32> {
  0..(100 as u32)
}
}

value

Form: #[proptest(value = V)] or #[proptest(value(V))], where V can be: (a) a Rust expression enclosed in a string; (b) another literal, or (c) a bare identifier naming a no-argument function.

Usable on: enum variants, fields

The value modifier indicates that proptest should use the given expression or function to produce a value for the field, instead of going through the usual value generation machinery.

The argument to value is directly used as an expression for the field value or enum variant to be generated, except that in the third form where it is a bare identifier, it is called as a no-argument function to produce the value.

Using value is equivalent to using strategy and enclosing the value in LazyJust.

Example:

#![allow(unused)]
fn main() {
extern crate proptest_derive;
extern crate proptest;
use proptest_derive::Arbitrary;
use proptest::prelude::*;
use std::time::Instant;

#[derive(Debug, Arbitrary)]
struct EventCounter {
    // We always start with the first two fields set to 0/None
    #[proptest(value = 0)]
    number_seen: u64,

    #[proptest(value = "None")]
    last_seen_time: Option<Instant>,

    // This field is generated normally
    max_events: u64,
}
}

weight

Form: #[proptest(weight = W)] or #[proptest(weight(W))], where W is an expression evaluating to a u32. weight may also be abbreviated to w, as in #[proptest(w = W)].

Usable on: enum variants

The weight modifier determines how likely proptest is to generate a particular enum variant. Weights are relative to each other; for example, a weight = 3 variant is 50% more likely to be generated than a weight = 2 variant and three times as likely to be generated as a weight = 1 variant.

Variants with no weight modifier are equivalent to being annotated #[proptest(weight = 1)].

Example:

#![allow(unused)]
fn main() {
extern crate proptest_derive;
extern crate proptest;
use proptest_derive::Arbitrary;
use proptest::proptest;
#[derive(Debug, Arbitrary)]
enum FilterOption {
    KeepAll,
    DiscardAll,

    // This option is presumably harder for the code to handle correctly,
    // so we generate it more frequently than the other options.
    #[proptest(weight = 3)]
    OnlyMatching(String),
}
}

Error Index

E0001

This error occurs when #[derive(Arbitrary)] is used on a type which has any lifetime parameters. For example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Foo<'a> {
    bar: &'a str,
}
}

It is not yet possible to define a Strategy which generates a type that is lifetime-generic (e.g. &'a T). Thus, proptest cannot implement Arbitrary for such types either and therefore you cannot #[derive(Arbitrary)] for such types. GATs are available in stable rust as of 1.65 and we will be revisiting how to support this. To follow the progress, consult the tracking issue on the matter.

E0002

This error occurs when #[derive(Arbitrary)] is used on a union type. An example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
union IU32 {
    signed: i32,
    unsigned: u32,
}
}

There are two main reasons for the error.

1. It is not possible to #[derive(Debug)] on union types and manual implementations cannot know which variant is valid so there are not many valid implementations which are possible.

2. Second, we cannot mechanically tell which variant out of signed and unsigned to generate. While we could allow you to tell the macro, with an attribute such as #[proptest(select)] on the variant, we have opted for a more conservative approach for the time being. If you have a use case for #[derive(Arbitrary)] on union types, please reach out on the issue tracker.

E0003

This error occurs when #[derive(Arbitrary)] is used on a struct which contains known uninhabited types. This in turn means the struct itself is uninhabited and so it there is no sensible Arbitrary implementation since values of the struct cannot be produced.

A trivial example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Uninhabited {
    inhabited: u32,
    never: !,
}
}

Because there exist no values assignable to field never, it is also impossible to construct an instance of struct Uninhabited.

Proptest’s ability to identify uninhabited types is limited. If it does not recognise a particular type as uninhabited, the type will instead be assumed to be inhabited and you will instead get an error about the type not implementing Arbitrary trait.

E0004

This error occurs when #[derive(Arbitrary)] is used on an enum with no variants at all. For example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
enum Uninhabited {}
}

Such an enum has no values at all, so it does not make sense to provide an Arbitrary implementation for it since no values can be generated.

E0005

This error occurs if #[derive(Arbitrary)] is used on an enum whose variants are all uninhabited, using the same logic as described for E0003. As a result, the enum itself is totally uninhabited.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
enum Uninhabited {
    Never(!),
    NeverEver(!, !),
}
}

E0006

This error occurs if #[derive(Arbitrary)] is used on an enum where all inhabited variants are marked with [#[proptest(skip)]]. In other words, proptest is forbidden from generating any of the enum’s variants, and thus the enum itself cannot be generated.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
enum MyEnum {
    // Ordinarily, proptest would be able to generate either of these variants,
    // but both are forbidden, so in the end proptest isn't allowed to generate
    // anything at all.
    #[proptest(skip)]
    UnitVariant,
    #[proptest(skip)]
    SimpleVariant(u32),
    // This variant is implicitly skipped because proptest knows it is
    // uninhabited.
    Uninhabited(!),
}
}

E0007

This error happens if an attribute [#[proptest(strategy = "expr")]] or [#[proptest(value = "expr")]] is applied to the same item that has #[derive(Arbitrary)].

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
#[proptest(value = "MyStruct(42)")]
struct MyStruct(u32);
}

This is rejected since nothing is being “derived” per se. A written out implementation of Arbitrary should be used instead.

E0008

This error happens if [#[proptest(skip)]] is applied to an unskippable item. For example, struct fields cannot be skipped because Rust requires every field of a struct to have a value.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct WidgetContainer {
    desired_widget_count: usize,
    #[proptest(skip)]
    widgets: Vec<Widget>,
}
}

In general, the appropriate way to request proptest to not generate a field value is to use [#[proptest(value = "expr")]] to provide a fixed value yourself. For example, the above code could be properly written as follows:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct WidgetContainer {
    desired_widget_count: usize,
    #[proptest(value = "vec![]")] // Always generate an empty widget vec
    widgets: Vec<Widget>,
}
}

E0009

This error happens if [#[proptest(weight = <integer>)]] is applied to an item where this does not make sense, such as a struct field. For example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Point {
    x: u32,
    #[proptest(weight = 42)]
    y: u32,
}
}

The weight attribute only is sensible where proptest has a choice between multiple items, i.e., enum variants. In contrast, with struct fields proptest must provide a value for every field so there is no “this-or-that” choice.

E0010

This error occurs if [#[proptest(params = "type")]] and/or [#[proptest(no_params)]] are set on both an item and its parent.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
#[proptest(params = "String")]
struct Foo {
    #[proptest(no_params)]
    bar: String,
}
}

If the parent item has any explicit parameter configuration, it totally defines the parameters for the whole Arbitrary implementation and the child items must work with that and cannot specify their own parameters.

E0011

This error occurs if [#[proptest(params = "type")]] is set on a field but no explicit strategy is configured with [#[proptest(strategy = "expr")]] or another such modifier. For example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Foo {
    #[proptest(param = "u8")]
    some_string: String,
}
}

This example illustrates why both must be specified: String’s arbitrary implementation takes a proptest::string::StringParam, but here we try to pass it a u8.

While the generated code could work if the type given by param is the same as that for the default strategy, there would be no purpose in specifying the parameter type by hand; therefore specifying only param is in all cases forbidden.

E0012

This error occurs if [#[proptest(filter = "expr")]] is set on an item, but the item containing it specifies a direct way to generate the whole value, which would thus occur without consulting the filter.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
enum Foo {
    #[proptest(value = "Foo::Bar(42)")]
    Bar {
        #[proptest(filter = "is_even")]
        even_number: u32,
    },
    // ...
}
}

In this example, the entire Bar variant specifies how to generate itself wholesale. As a result, the filter clause on even_number has no opportunity to run.

E0013

This error would occur if an outer attribute of the form #![proptest(..)] were applied to something underneath a #[derive(Arbitrary)].

As of Rust 1.30.0, there are no known ways to produce this error since the Rust compiler will reject the attribute first.

E0014

This error occurs if a bare #[proptest] attribute is applied to anything, since it has no meaningful content.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Foo {
    #[proptest]
    field: u8,
}
}

The only legal use of the attribute is the form #[proptest(..)].

E0015

This error occurs if an attribute of the form #[proptest = value] is encountered in any context.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Foo {
    #[proptest = 1234]
    field: u8,
}
}

E0016

This error occurs if a literal (as opposed to key = value) is passed inside #[proptest(..)] in any context.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Foo {
    #[proptest(1234)]
    field: u8,
}
}

E0017

This error occurs if any modifier of #[proptest(..)] is set more than once on the same item.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
#[proptest(no_params, no_params)]
struct Foo(u32);
}

E0018

This error occurs if an unknown modifier is passed in #[proptest(..)].

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
#[proptest(frobnicate = "true")]
struct Foo(u32);
}

Please see the modifiers reference to see what modifiers are available.

E0019

This error happens if anything extra is passed to [#[proptest(no_params)]].

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
#[proptest(no_params = "true")]
struct Foo(u32);
}

no_params takes no configuration. The correct form is simply #[proptest(no_params)].

E0020

This error happens if anything extra is passed to [#[proptest(skip)]].

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
enum Foo {
    Small,
    #[proptest(skip = "yes")]
    Huge(ExpensiveType),
}
}

skip takes no configuration. The correct form is simply #[proptest(skip)].

E0021

This error happens if [#[proptest(weight = <integer>)]] is passed an invalid integer or passed nothing at all.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
enum Foo {
    #[proptest(weight)]
    V1,
    #[proptest(weight = heavy)]
    V2,
}
}

The only acceptable form is #[proptest(weight = <integer>)], where <integer> is either an integer literal which fits in a u32 or the same but enclosed in quotation marks.

E0022

This error occurs if more than one of [#[proptest(no_params)]] and [#[proptest(params = "type")]] are applied to the same item.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
#[proptest(no_params, params = "u8")]
struct Foo(u32);
}

One attribute or the other must be picked depending on desired effect.

E0023

This error happens if an invalid [#[proptest(params = "type")]] attribute is applied to an item.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
#[proptest(params = "Vec<u8")] // Note missing '>'
struct Foo(u32);
}

There are a few different ways to get this error:

• Pass nothing at all. E.g., #[proptest(params)].

• Pass something other than a string as the value. E.g., #[proptest(params = 42)].

• Pass a malformed type in the string, as in the example above. (See also caveat on syntax.)

E0024

This error happens if an invalid #[proptest ..] attribute is applied using a syntax the proptest-derive crate is not prepared to handle.

Exactly what conditions can produce this error vary by Rust version.

E0025

This error happens if more than one of [#[proptest(strategy = "expr")]], [#[proptest(value = "expr")]], or [#[proptest(regex = "string")]] are applied to the same item.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Foo {
    #[proptest(value = "42", strategy = "Just(56)")]
    bar: u32,
}
}

Each of these modifiers completely describe how to generate the value, so they cannot both be applied to the same thing. One or the other must be chosen depending on the desired effect.

E0026

This error happens if an invalid form of [#[proptest(strategy = "expr")]] or [#[proptest(value = "expr")]] is used.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Foo {
    #[proptest(value = "3↑↑↑↑3")] // String content is not valid Rust syntax
    g1: u128,
}
}

There are a few different ways to get this error:

• Pass nothing at all. E.g., #[proptest(value)].

• Use another illegal form. E.g., #[proptest(value("a", "b"))].

• Pass a string expression which is not valid Rust syntax, as in the above example. (See also caveat on syntax.)

E0027

This error happens if an invalid form of [#[proptest(filter = "expr")]] is used.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Foo {
    #[proptest(filter = "> 3")] // String content is not an expression
    big_number: u128,
}
}

There are a few different ways to get this error:

• Pass nothing at all. E.g., #[proptest(filter)].

• Use another illegal form. E.g., #[proptest(filter("a", "b"))].

• Pass a string expression which is not valid Rust syntax, as in the above example. (See also caveat on syntax.)

E0028

This error occurs if a modifier which implies a value is to be generated is applied to an enum variant which is also marked [#[proptest(skip)]].

Example:

#![allow(unused)]

fn main() {
#[derive(Debug, Arbitrary)]
enum Enum {
    V1(u32),
    #[proptest(skip, value = "Enum::V2(42)")]
    V2(u32),
}
}

Here, the [#[proptest(value = "expr")]] modifier suggests the user intends some value to be generated for the enum variant, but at the same time [#[proptest(skip)]] indicates not to generate that variant.

E0029

This error happens if a modifier which would constrain or control how the value of an enum variant is to be generated is applied to a unit variant.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
enum Foo {
    #[proptest(value = "Foo::V1")]
    UnitVariant,
    // ...
}
}

Unit variants only have one possible value, so there is only one possible strategy. As a result, it is pointless to try to specify an alternate strategy or to filter such variants.

E0030

This error happens if a modifier which would constrain or control how the value of a struct is to be generated is applied to a unit struct.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
#[proptest(params = "u8")]
struct UnitStruct;
}

Unit structs only have one possible value, so there is only one possible strategy. As a result, it is pointless to try to specify an alternate strategy or to filter such structs.

E0031

This error occurs if [#[proptest(no_bound)]] is applied to something that is not a type variable.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Foo {
    #[proptest(no_bound)]
    bar: u32,
}
}

The no_bound modifier only makes sense on generic type variables, as in

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Foo<#[proptest(no_bound)] T> {
    #[proptest(value = "None")]
    bar: Option<T>,
}
}

E0032

This error happens if [#[proptest(no_bound)]] is passed anything.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
struct Foo<#[proptest(no_bound = "yes")] T> {
    _bar: PhantomData<T>,
}
}

The only valid form for the modifier is #[proptest(no_bound)].

E0033

This error occurs if the sum of the weights on the variants of an enum overflow a u32.

Example:

#![allow(unused)]
fn main() {
#[derive(Debug, Arbitrary)]
enum Foo {
    #[proptest(weight = 3_000_000_000)]
    ThreeFifths,
    #[proptest(weight = 2_000_000_000)]
    TwoFifths,
}
}

The only solution is to reduce the magnitude of the weights so that their sum fits in a u32. Keep in mind that variants without a weight modifier still effectively have #[proptest(weight = 1)].

E0034

This error occurs if [#[proptest(regex = "string")]] is used with invalid syntax.

The most common forms are #[proptest(regex = "string-regex")] and #[proptest(regex("string-regex"))].

E0035

This error occurs if both [#[proptest(regex = "string")]] and [#[proptest(params = "type")]] are applied to the same item.

Values generated via regular expression take no parameters so the params modifier would be meaningless.

“Valid Rust syntax”

The definition of “valid Rust syntax” in various string modifiers is determined by the syn crate. If valid syntax is rejected, you can work around it in a couple ways depending on what the syntax is describing:

For types, simply define a type alias for the type in question. For example,

#![allow(unused)]
fn main() {
type RetroBox = ~str; // N.B. "~str" is not valid Rust 1.30 syntax

//...
#[derive(Debug, Arbitrary)]
#[proptest(params = "RetroBox")]
struct MyStruct { /* ... */ }
}

For values, you can generally factor the code into a constant or function. For example,

#![allow(unused)]
fn main() {
// N.B. Rust 1.30 does not have an exponentiation operator.
const PI_SQUARED: f64 = PI ** 2.0;

//...
#[derive(Debug, Arbitrary)]
struct MyStruct {
    #[proptest(value = "PI_SQUARED")]
    factor: f64,
}
}

If you need to implement such a work around, consider also filing an issue.

[#[proptest(filter = "expr")]]: modifiers.md#filter [#[proptest(no_bound)]]: modifiers.md#no_bound [#[proptest(no_params)]]: modifiers.md#no_params [#[proptest(params = "type")]]: modifiers.md#params [#[proptest(regex = "string")]]: modifiers.md#regex [#[proptest(skip)]]: modifiers.md#skip [#[proptest(strategy = "expr")]]: modifiers.md#strategy [#[proptest(value = "expr")]]: modifiers.md#value [#[proptest(weight = <integer>)]]: modifiers.md#weight