# Unit Testing

## Testing Base Julia

Julia is under rapid development and has an extensive test suite to verify functionality across multiple platforms. If you build Julia from source, you can run this test suite with `make test`. In a binary install, you can run the test suite using `Base.runtests()`.

`Base.runtests` — Function

    Base.runtests(tests=["all"]; ncores=ceil(Int, Sys.CPU_THREADS / 2),
                  exit_on_error=false, revise=false, [seed])

Run the Julia unit tests listed in `tests`, which can be either a string or an array of strings, using `ncores` processors. If `exit_on_error` is `false`, when one test fails, all remaining tests in other files will still be run; they are otherwise discarded, when `exit_on_error == true`. If `revise` is `true`, the `Revise` package is used to load any modifications to `Base` or to the standard libraries before running the tests. If a seed is provided via the keyword argument, it is used to seed the global RNG in the context where the tests are run; otherwise the seed is chosen randomly.

source

## Basic Unit Tests

The `Test` module provides simple unit testing functionality. Unit testing is a way to see if your code is correct by checking that the results are what you expect. It can be helpful to ensure your code still works after you make changes, and can be used when developing as a way of specifying the behaviors your code should have when complete.

Simple unit testing can be performed with the `@test` and `@test_throws` macros:

`Test.@test` — Macro

    @test ex
    @test f(args...) key=val ...
    @test ex broken=true
    @test ex skip=true

Tests that the expression `ex` evaluates to `true`. Returns a `Pass` `Result` if it does, a `Fail` `Result` if it is `false`, and an `Error` `Result` if it could not be evaluated.

Examples

    julia> @test true
    Test Passed
      Expression: true

    julia> @test [1, 2] + [2, 1] == [3, 3]
    Test Passed
      Expression: [1, 2] + [2, 1] == [3, 3]
       Evaluated: [3, 3] == [3, 3]

The `@test f(args...) key=val...` form is equivalent to writing `@test f(args..., key=val...)` which can be useful when the expression is a call using infix syntax such as approximate comparisons:

    julia> @test π ≈ 3.14 atol=0.01
    Test Passed
      Expression: ≈(π, 3.14, atol = 0.01)
       Evaluated: ≈(π, 3.14; atol = 0.01)

This is equivalent to the uglier test `@test ≈(π, 3.14, atol=0.01)`. It is an error to supply more than one expression unless the first is a call expression and the rest are assignments (`k=v`).

You can use any key for the `key=val` arguments, except for `broken` and `skip`, which have special meanings in the context of `@test`:

- `broken=cond` indicates a test that should pass but currently consistently fails when `cond==true`. Tests that the expression `ex` evaluates to `false` or causes an exception. Returns a `Broken` `Result` if it does, or an `Error` `Result` if the expression evaluates to `true`. Regular `@test ex` is evaluated when `cond==false`.
- `skip=cond` marks a test that should not be executed but should be included in test summary reporting as `Broken`, when `cond==true`. This can be useful for tests that intermittently fail, or tests of not-yet-implemented functionality. Regular `@test ex` is evaluated when `cond==false`.

Examples

    julia> @test 2 + 2 ≈ 6 atol=1 broken=true
    Test Broken
      Expression: ≈(2 + 2, 6, atol = 1)

    julia> @test 2 + 2 ≈ 5 atol=1 broken=false
    Test Passed
      Expression: ≈(2 + 2, 5, atol = 1)
       Evaluated: ≈(4, 5; atol = 1)

    julia> @test 2 + 2 == 5 skip=true
    Test Broken
      Skipped: 2 + 2 == 5

    julia> @test 2 + 2 == 4 skip=false
    Test Passed
      Expression: 2 + 2 == 4
       Evaluated: 4 == 4

Julia 1.7

The `broken` and `skip` keyword arguments require at least Julia 1.7.

source
`Test.@test_throws` — Macro

    @test_throws exception expr

Tests that the expression `expr` throws `exception`. The exception may specify either a type, or a value (which will be tested for equality by comparing fields). Note that `@test_throws` does not support a trailing keyword form.

Examples

    julia> @test_throws BoundsError [1, 2, 3][4]
    Test Passed
      Expression: ([1, 2, 3])[4]
          Thrown: BoundsError

    julia> @test_throws DimensionMismatch [1, 2, 3] + [1, 2]
    Test Passed
      Expression: [1, 2, 3] + [1, 2]
          Thrown: DimensionMismatch

source

For example, suppose we want to check our new function `foo(x)` works as expected:

    julia> using Test

    julia> foo(x) = length(x)^2
    foo (generic function with 1 method)

If the condition is true, a `Pass` is returned:

    julia> @test foo("bar") == 9
    Test Passed
      Expression: foo("bar") == 9
       Evaluated: 9 == 9

    julia> @test foo("fizz") >= 10
    Test Passed
      Expression: foo("fizz") >= 10
       Evaluated: 16 >= 10

If the condition is false, then a `Fail` is returned and an exception is thrown:

    julia> @test foo("f") == 20
    Test Failed at none:1
      Expression: foo("f") == 20
       Evaluated: 1 == 20
    ERROR: There was an error during testing

If the condition could not be evaluated because an exception was thrown, which occurs in this case because `length` is not defined for symbols, an `Error` object is returned and an exception is thrown:

    julia> @test foo(:cat) == 1
    Error During Test
      Test threw an exception of type MethodError
      Expression: foo(:cat) == 1
      MethodError: no method matching length(::Symbol)
      Closest candidates are:
        length(::SimpleVector) at essentials.jl:256
        length(::Base.MethodList) at reflection.jl:521
        length(::MethodTable) at reflection.jl:597
        ...
      Stacktrace:
      [...]
    ERROR: There was an error during testing

If we expect that evaluating an expression should throw an exception, then we can use `@test_throws` to check that this occurs:

    julia> @test_throws MethodError foo(:cat)
    Test Passed
      Expression: foo(:cat)
          Thrown: MethodError

## Working with Test Sets

Typically a large number of tests are used to make sure functions work correctly over a range of inputs. In the event a test fails, the default behavior is to throw an exception immediately. However, it is normally preferable to run the rest of the tests first to get a better picture of how many errors there are in the code being tested.

Note

The `@testset` will create a local scope of its own when running the tests in it.

The `@testset` macro can be used to group tests into sets. All the tests in a test set will be run, and at the end of the test set a summary will be printed. If any of the tests failed, or could not be evaluated due to an error, the test set will then throw a `TestSetException`.

`Test.@testset` — Macro

    @testset [CustomTestSet] [option=val  ...] ["description"] begin ... end
    @testset [CustomTestSet] [option=val  ...] ["description $v"] for v in (...) ... end
    @testset [CustomTestSet] [option=val  ...] ["description $v, $w"] for v in (...), w in (...) ... end

Starts a new test set, or multiple test sets if a `for` loop is provided.

If no custom testset type is given it defaults to creating a `DefaultTestSet`. `DefaultTestSet` records all the results and, if there are any `Fail`s or `Error`s, throws an exception at the end of the top-level (non-nested) test set, along with a summary of the test results.

Any custom testset type (subtype of `AbstractTestSet`) can be given and it will also be used for any nested `@testset` invocations. The given options are only applied to the test set where they are given. The default test set type accepts the `verbose` boolean option: if `true`, the result summary of the nested testsets is shown even when they all pass (the default is `false`).

The description string accepts interpolation from the loop indices. If no description is provided, one is constructed based on the variables.

By default the `@testset` macro will return the testset object itself, though this behavior can be customized in other testset types. If a `for` loop is used then the macro collects and returns a list of the return values of the `finish` method, which by default will return a list of the testset objects used in each iteration.

Before the execution of the body of a `@testset`, there is an implicit call to `Random.seed!(seed)` where `seed` is the current seed of the global RNG. Moreover, after the execution of the body, the state of the global RNG is restored to what it was before the `@testset`. This is meant to ease reproducibility in case of failure, and to allow seamless re-arrangements of `@testset`s regardless of their side-effect on the global RNG state.

Examples

    julia> @testset "trigonometric identities" begin
               θ = 2/3*π
               @test sin(-θ) ≈ -sin(θ)
               @test cos(-θ) ≈ cos(θ)
               @test sin(2θ) ≈ 2*sin(θ)*cos(θ)
               @test cos(2θ) ≈ cos(θ)^2 - sin(θ)^2
           end;
    Test Summary:            | Pass  Total
    trigonometric identities |    4      4

source
`Test.TestSetException` — Type

    TestSetException

Thrown when a test set finishes and not all tests passed.

source

We can put our tests for the `foo(x)` function in a test set:

    julia> @testset "Foo Tests" begin
               @test foo("a")   == 1
               @test foo("ab")  == 4
               @test foo("abc") == 9
           end;
    Test Summary: | Pass  Total
    Foo Tests     |    3      3

Test sets can also be nested:

    julia> @testset "Foo Tests" begin
               @testset "Animals" begin
                   @test foo("cat") == 9
                   @test foo("dog") == foo("cat")
               end
               @testset "Arrays $i" for i in 1:3
                   @test foo(zeros(i)) == i^2
                   @test foo(fill(1.0, i)) == i^2
               end
           end;
    Test Summary: | Pass  Total
    Foo Tests     |    8      8

In the event that a nested test set has no failures, as happened here, it will be hidden in the summary, unless the `verbose=true` option is passed:

    julia> @testset verbose = true "Foo Tests" begin
               @testset "Animals" begin
                   @test foo("cat") == 9
                   @test foo("dog") == foo("cat")
               end
               @testset "Arrays $i" for i in 1:3
                   @test foo(zeros(i)) == i^2
                   @test foo(fill(1.0, i)) == i^2
               end
           end;
    Test Summary: | Pass  Total
    Foo Tests     |    8      8
      Animals     |    2      2
      Arrays 1    |    2      2
      Arrays 2    |    2      2
      Arrays 3    |    2      2

If we do have a test failure, only the details for the failed test sets will be shown:

    julia> @testset "Foo Tests" begin
               @testset "Animals" begin
                   @testset "Felines" begin
                       @test foo("cat") == 9
                   end
                   @testset "Canines" begin
                       @test foo("dog") == 9
                   end
               end
               @testset "Arrays" begin
                   @test foo(zeros(2)) == 4
                   @test foo(fill(1.0, 4)) == 15
               end
           end

    Arrays: Test Failed
      Expression: foo(fill(1.0, 4)) == 15
       Evaluated: 16 == 15
    [...]
    Test Summary: | Pass  Fail  Total
    Foo Tests     |    3     1      4
      Animals     |    2            2
      Arrays      |    1     1      2
    ERROR: Some tests did not pass: 3 passed, 1 failed, 0 errored, 0 broken.

## Other Test Macros

As calculations on floating-point values can be imprecise, you can perform approximate equality checks using either `@test a ≈ b` (where `≈`, typed via tab completion of `\approx`, is the `isapprox` function) or use `isapprox` directly.

    julia> @test 1 ≈ 0.999999999
    Test Passed
      Expression: 1 ≈ 0.999999999
       Evaluated: 1 ≈ 0.999999999

    julia> @test 1 ≈ 0.999999
    Test Failed at none:1
      Expression: 1 ≈ 0.999999
       Evaluated: 1 ≈ 0.999999
    ERROR: There was an error during testing

You can specify relative and absolute tolerances by setting the `rtol` and `atol` keyword arguments of `isapprox`, respectively, after the `≈` comparison:

    julia> @test 1 ≈ 0.999999  rtol=1e-5
    Test Passed
      Expression: ≈(1, 0.999999, rtol = 1.0e-5)
       Evaluated: ≈(1, 0.999999; rtol = 1.0e-5)

Note that this is not a specific feature of the `≈` but rather a general feature of the `@test` macro: `@test a <op> b key=val` is transformed by the macro into `@test op(a, b, key=val)`. It is, however, particularly useful for `≈` tests.

`Test.@inferred` — Macro

    @inferred [AllowedType] f(x)

Tests that the call expression `f(x)` returns a value of the same type inferred by the compiler. It is useful to check for type stability.

`f(x)` can be any call expression. Returns the result of `f(x)` if the types match, and an `Error` `Result` if it finds different types.

Optionally, `AllowedType` relaxes the test, by making it pass when either the type of `f(x)` matches the inferred type modulo `AllowedType`, or when the return type is a subtype of `AllowedType`. This is useful when testing type stability of functions returning a small union such as `Union{Nothing, T}` or `Union{Missing, T}`.

    julia> f(a) = a > 1 ? 1 : 1.0
    f (generic function with 1 method)

    julia> typeof(f(2))
    Int64

    julia> @code_warntype f(2)
    MethodInstance for f(::Int64)
      from f(a) in Main at none:1
    Arguments
      #self#::Core.Const(f)
      a::Int64
    Body::UNION{FLOAT64, INT64}
    1 ─ %1 = (a > 1)::Bool
    └──      goto #3 if not %1
    2 ─      return 1
    3 ─      return 1.0

    julia> @inferred f(2)
    ERROR: return type Int64 does not match inferred return type Union{Float64, Int64}
    [...]

    julia> @inferred max(1, 2)
    2

    julia> g(a) = a < 10 ? missing : 1.0
    g (generic function with 1 method)

    julia> @inferred g(20)
    ERROR: return type Float64 does not match inferred return type Union{Missing, Float64}
    [...]

    julia> @inferred Missing g(20)
    1.0

    julia> h(a) = a < 10 ? missing : f(a)
    h (generic function with 1 method)

    julia> @inferred Missing h(20)
    ERROR: return type Int64 does not match inferred return type Union{Missing, Float64, Int64}
    [...]

source
`Test.@test_logs` — Macro

    @test_logs [log_patterns...] [keywords] expression

Collect a list of log records generated by `expression` using `collect_test_logs`, check that they match the sequence `log_patterns`, and return the value of `expression`. The `keywords` provide some simple filtering of log records: the `min_level` keyword controls the minimum log level which will be collected for the test, the `match_mode` keyword defines how matching will be performed (the default `:all` checks that all logs and patterns match pairwise; use `:any` to check that the pattern matches at least once somewhere in the sequence.)

The most useful log pattern is a simple tuple of the form `(level,message)`. A different number of tuple elements may be used to match other log metadata, corresponding to the arguments to passed to `AbstractLogger` via the `handle_message` function: `(level,message,module,group,id,file,line)`. Elements which are present will be matched pairwise with the log record fields using `==` by default, with the special cases that `Symbol`s may be used for the standard log levels, and `Regex`s in the pattern will match string or Symbol fields using `occursin`.

Examples

Consider a function which logs a warning, and several debug messages:

    function foo(n)
        @info "Doing foo with n=$n"
        for i=1:n
            @debug "Iteration $i"
        end
        42
    end

We can test the info message using

    @test_logs (:info,"Doing foo with n=2") foo(2)

If we also wanted to test the debug messages, these need to be enabled with the `min_level` keyword:

    @test_logs (:info,"Doing foo with n=2") (:debug,"Iteration 1") (:debug,"Iteration 2") min_level=Logging.Debug foo(2)

If you want to test that some particular messages are generated while ignoring the rest, you can set the keyword `match_mode=:any`:

    @test_logs (:info,) (:debug,"Iteration 42") min_level=Logging.Debug match_mode=:any foo(100)

The macro may be chained with `@test` to also test the returned value:

    @test (@test_logs (:info,"Doing foo with n=2") foo(2)) == 42

If you want to test for the absence of warnings, you can omit specifying log patterns and set the `min_level` accordingly:

    # test that the expression logs no messages when the logger level is warn:
    @test_logs min_level=Logging.Warn @info("Some information") # passes
    @test_logs min_level=Logging.Warn @warn("Some information") # fails

If you want to test the absence of warnings (or error messages) in `stderr` which are not generated by `@warn`, see `@test_nowarn`.

source
`Test.@test_deprecated` — Macro

    @test_deprecated [pattern] expression

When `--depwarn=yes`, test that `expression` emits a deprecation warning and return the value of `expression`. The log message string will be matched against `pattern` which defaults to `r"deprecated"i`.

When `--depwarn=no`, simply return the result of executing `expression`. When `--depwarn=error`, check that an ErrorException is thrown.

Examples

    # Deprecated in julia 0.7
    @test_deprecated num2hex(1)

    # The returned value can be tested by chaining with @test:
    @test (@test_deprecated num2hex(1)) == "0000000000000001"

source
`Test.@test_warn` — Macro

    @test_warn msg expr

Test whether evaluating `expr` results in `stderr` output that contains the `msg` string or matches the `msg` regular expression. If `msg` is a boolean function, tests whether `msg(output)` returns `true`. If `msg` is a tuple or array, checks that the error output contains/matches each item in `msg`. Returns the result of evaluating `expr`.

See also `@test_nowarn` to check for the absence of error output.

Note: Warnings generated by `@warn` cannot be tested with this macro. Use `@test_logs` instead.

source
`Test.@test_nowarn` — Macro

    @test_nowarn expr

Test whether evaluating `expr` results in empty `stderr` output (no warnings or other messages). Returns the result of evaluating `expr`.

Note: The absence of warnings generated by `@warn` cannot be tested with this macro. Use `@test_logs` instead.

source

## Broken Tests

If a test fails consistently it can be changed to use the `@test_broken` macro. This will denote the test as `Broken` if the test continues to fail and alerts the user via an `Error` if the test succeeds.

`Test.@test_broken` — Macro

    @test_broken ex
    @test_broken f(args...) key=val ...

Indicates a test that should pass but currently consistently fails. Tests that the expression `ex` evaluates to `false` or causes an exception. Returns a `Broken` `Result` if it does, or an `Error` `Result` if the expression evaluates to `true`. This is equivalent to `@test ex broken=true`.

The `@test_broken f(args...) key=val...` form works as for the `@test` macro.

Examples

    julia> @test_broken 1 == 2
    Test Broken
      Expression: 1 == 2

    julia> @test_broken 1 == 2 atol=0.1
    Test Broken
      Expression: ==(1, 2, atol = 0.1)

source

`@test_skip` is also available to skip a test without evaluation, but counting the skipped test in the test set reporting. The test will not run but gives a `Broken` `Result`.

`Test.@test_skip` — Macro

    @test_skip ex
    @test_skip f(args...) key=val ...

Marks a test that should not be executed but should be included in test summary reporting as `Broken`. This can be useful for tests that intermittently fail, or tests of not-yet-implemented functionality. This is equivalent to `@test ex skip=true`.

The `@test_skip f(args...) key=val...` form works as for the `@test` macro.

Examples

    julia> @test_skip 1 == 2
    Test Broken
      Skipped: 1 == 2

    julia> @test_skip 1 == 2 atol=0.1
    Test Broken
      Skipped: ==(1, 2, atol = 0.1)

source

## Creating Custom `AbstractTestSet` Types

Packages can create their own `AbstractTestSet` subtypes by implementing the `record` and `finish` methods. The subtype should have a one-argument constructor taking a description string, with any options passed in as keyword arguments.

`Test.record` — Function

    record(ts::AbstractTestSet, res::Result)

Record a result to a testset. This function is called by the `@testset` infrastructure each time a contained `@test` macro completes, and is given the test result (which could be an `Error`). This will also be called with an `Error` if an exception is thrown inside the test block but outside of a `@test` context.

source
`Test.finish` — Function

    finish(ts::AbstractTestSet)

Do any final processing necessary for the given testset. This is called by the `@testset` infrastructure after a test block executes.

Custom `AbstractTestSet` subtypes should call `record` on their parent (if there is one) to add themselves to the tree of test results. This might be implemented as:

    if get_testset_depth() != 0
        # Attach this test set to the parent test set
        parent_ts = get_testset()
        record(parent_ts, self)
        return self
    end

source

`Test` takes responsibility for maintaining a stack of nested testsets as they are executed, but any result accumulation is the responsibility of the `AbstractTestSet` subtype. You can access this stack with the `get_testset` and `get_testset_depth` methods. Note that these functions are not exported.

`Test.get_testset` — Function

    get_testset()

Retrieve the active test set from the task's local storage. If no test set is active, use the fallback default test set.

source
`Test.get_testset_depth` — Function

    get_testset_depth()

Returns the number of active test sets, not including the default test set

source

`Test` also makes sure that nested `@testset` invocations use the same `AbstractTestSet` subtype as their parent unless it is set explicitly. It does not propagate any properties of the testset. Option inheritance behavior can be implemented by packages using the stack infrastructure that `Test` provides.

Defining a basic `AbstractTestSet` subtype might look like:

    import Test: Test, record, finish
    using Test: AbstractTestSet, Result, Pass, Fail, Error
    using Test: get_testset_depth, get_testset
    struct CustomTestSet <: Test.AbstractTestSet
        description::AbstractString
        foo::Int
        results::Vector
        # constructor takes a description string and options keyword arguments
        CustomTestSet(desc; foo=1) = new(desc, foo, [])
    end

    record(ts::CustomTestSet, child::AbstractTestSet) = push!(ts.results, child)
    record(ts::CustomTestSet, res::Result) = push!(ts.results, res)
    function finish(ts::CustomTestSet)
        # just record if we're not the top-level parent
        if get_testset_depth() > 0
            record(get_testset(), ts)
        end
        ts
    end

And using that testset looks like:

    @testset CustomTestSet foo=4 "custom testset inner 2" begin
        # this testset should inherit the type, but not the argument.
        @testset "custom testset inner" begin
            @test true
        end
    end

## Test utilities

`Test.GenericArray` — Type

The `GenericArray` can be used to test generic array APIs that program to the `AbstractArray` interface, in order to ensure that functions can work with array types besides the standard `Array` type.

source
`Test.GenericDict` — Type

The `GenericDict` can be used to test generic dict APIs that program to the `AbstractDict` interface, in order to ensure that functions can work with associative types besides the standard `Dict` type.

source
`Test.GenericOrder` — Type

The `GenericOrder` can be used to test APIs for their support of generic ordered types.

source
`Test.GenericSet` — Type

The `GenericSet` can be used to test generic set APIs that program to the `AbstractSet` interface, in order to ensure that functions can work with set types besides the standard `Set` and `BitSet` types.

source
`Test.GenericString` — Type

The `GenericString` can be used to test generic string APIs that program to the `AbstractString` interface, in order to ensure that functions can work with string types besides the standard `String` type.

source
`Test.detect_ambiguities` — Function

    detect_ambiguities(mod1, mod2...; recursive=false, ambiguous_bottom=false)

Returns a vector of `(Method,Method)` pairs of ambiguous methods defined in the specified modules. Use `recursive=true` to test in all submodules.

`ambiguous_bottom` controls whether ambiguities triggered only by `Union{}` type parameters are included; in most cases you probably want to set this to `false`. See `Base.isambiguous`.

source
`Test.detect_unbound_args` — Function

    detect_unbound_args(mod1, mod2...; recursive=false)

Returns a vector of `Method`s which may have unbound type parameters. Use `recursive=true` to test in all submodules.

source

Theme

This document was generated with Documenter.jl version 0.27.10 on Monday 7 February 2022. Using Julia version 1.7.2.