Doc unit testing

Unit testing in Rust

Rust has built in support for simple unit testing. Functions can be marked as unit tests using the 'test' attribute.

#[test]
fn return_none_if_empty() {
    // ... test code ...
}

A test function's signature must have no arguments and no return value. To run the tests in a crate, it must be compiled with the '--test' flag: rustc myprogram.rs --test -o myprogram-tests. Running the resulting executable will run all the tests in the crate. A test is considered successful if its function returns; if the task running the test fails, through a call to fail!, a failed check or assert, or some other means, then the test fails.

When compiling a crate with the '--test' flag '--cfg test' is also implied, so that tests can be conditionally compiled.

#[cfg(test)]
mod tests {
    #[test]
    fn return_none_if_empty() {
      // ... test code ...
    }
}

Additionally #[test] items behave as if they also have the #[cfg(test)] attribute, and will not be compiled when the --test flag is not used.

Tests that should not be run can be annotated with the 'ignore' attribute. The existence of these tests will be noted in the test runner output, but the test will not be run. Tests can also be ignored by configuration so, for example, to ignore a test on windows you can write #[ignore(cfg(target_os = "win32"))].

Tests that are intended to fail can be annotated with the 'should_fail' attribute. The test will be run, and if it causes its task to fail then the test will be counted as successful; otherwise it will be counted as a failure. For example:

#[test]
#[should_fail]
fn test_out_of_bounds_failure() {
    let v: [int] = [];
    v[0];
}

A test runner built with the '--test' flag supports a limited set of arguments to control which tests are run: the first free argument passed to a test runner specifies a filter used to narrow down the set of tests being run; the '--ignored' flag tells the test runner to run only tests with the 'ignore' attribute.

Parallelism

By default, tests are run in parallel, which can make interpreting failure output difficult. In these cases you can set the RUST_TEST_TASKS environment variable to 1 to make the tests run sequentially.

Benchmarking

The test runner also understands a simple form of benchmark execution. Benchmark functions are marked with the #[bench] attribute, rather than #[test], and have a different form and meaning. They are compiled along with #[test] functions when a crate is compiled with --test, but they are not run by default. To run the benchmark component of your testsuite, pass --bench to the compiled test runner.

The type signature of a benchmark function differs from a unit test: it takes a mutable reference to type test::BenchHarness. Inside the benchmark function, any time-variable or "setup" code should execute first, followed by a call to iter on the benchmark harness, passing a closure that contains the portion of the benchmark you wish to actually measure the per-iteration speed of.

For benchmarks relating to processing/generating data, one can set the bytes field to the number of bytes consumed/produced in each iteration; this will used to show the throughput of the benchmark. This must be the amount used in each iteration, not the total amount.

For example:

extern mod extra;
use std::vec;

#[bench]
fn bench_sum_1024_ints(b: &mut extra::test::BenchHarness) {
    let v = vec::from_fn(1024, |n| n);
    do b.iter {
        v.iter().fold(0, |old, new| old + *new);
    }
}

#[bench]
fn initialise_a_vector(b: &mut extra::test::BenchHarness) {
    do b.iter {
         vec::from_elem(1024, 0u64);
    }
    b.bytes = 1024 * 8;
}

The benchmark runner will calibrate measurement of the benchmark function to run the iter block "enough" times to get a reliable measure of the per-iteration speed.

Advice on writing benchmarks:

• Move setup code outside the iter loop; only put the part you want to measure inside
• Make the code do "the same thing" on each iteration; do not accumulate or change state
• Make the outer function idempotent too; the benchmark runner is likely to run it many times
• Make the inner iter loop short and fast so benchmark runs are fast and the calibrator can adjust the run-length at fine resolution
• Make the code in the iter loop do something simple, to assist in pinpointing performance improvements (or regressions)

To run benchmarks, pass the --bench flag to the compiled test-runner. Benchmarks are compiled-in but not executed by default.

Examples

Typical test run

> mytests

running 30 tests
running driver::tests::mytest1 ... ok
running driver::tests::mytest2 ... ignored
... snip ...
running driver::tests::mytest30 ... ok

result: ok. 28 passed; 0 failed; 2 ignored

Test run with failures

> mytests

running 30 tests
running driver::tests::mytest1 ... ok
running driver::tests::mytest2 ... ignored
... snip ...
running driver::tests::mytest30 ... FAILED

result: FAILED. 27 passed; 1 failed; 2 ignored

Running ignored tests

> mytests --ignored

running 2 tests
running driver::tests::mytest2 ... failed
running driver::tests::mytest10 ... ok

result: FAILED. 1 passed; 1 failed; 0 ignored

Running a subset of tests

> mytests mytest1

running 11 tests
running driver::tests::mytest1 ... ok
running driver::tests::mytest10 ... ignored
... snip ...
running driver::tests::mytest19 ... ok

result: ok. 11 passed; 0 failed; 1 ignored

Running benchmarks

> mytests --bench

running 2 tests
test bench_sum_1024_ints ... bench: 709 ns/iter (+/- 82)
test initialise_a_vector ... bench: 424 ns/iter (+/- 99) = 19320 MB/s

test result: ok. 0 passed; 0 failed; 0 ignored; 2 measured

Saving and ratcheting metrics

When running benchmarks or other tests, the test runner can record per-test "metrics". Each metric is a scalar f64 value, plus a noise value which represents uncertainty in the measurement. By default, all #[bench] benchmarks are recorded as metrics, which can be saved as JSON in an external file for further reporting.

In addition, the test runner supports ratcheting against a metrics file. Ratcheting is like saving metrics, except that after each run, if the output file already exists the results of the current run are compared against the contents of the existing file, and any regression causes the testsuite to fail. If the comparison passes -- if all metrics stayed the same (within noise) or improved -- then the metrics file is overwritten with the new values. In this way, a metrics file in your workspace can be used to ensure your work does not regress performance.

Test runners take 3 options that are relevant to metrics:

• --save-metrics=<file.json> will save the metrics from a test run to file.json
• --ratchet-metrics=<file.json> will ratchet the metrics against the file.json
• --ratchet-noise-percent=N will override the noise measurements in file.json, and consider a metric change less than N% to be noise. This can be helpful if you are testing in a noisy environment where the benchmark calibration loop cannot acquire a clear enough signal.