Structure test suite

You should ensure that assertions are kept at the end of test functions. This recommendation is so common that it has at least two names:

Arrange-Act-Assert (AAA)

became popular as part of test-driven development (TDD).

Given-When-Then (GWT)

is used in the context of behaviour-driven development (BDD).

The division into these free phases has many advantages. This separates the parts

Given/Arrange

The initial state. This is where you set up data or the environment to prepare the action.

When/Act

An action is executed. This is the focus of the test – the behaviour that we want to ensure works correctly.

Then/Assert

An expected result or end state should occur. At the end of the test, we make sure that the action has led to the expected behaviour.

A common counter-pattern is the Arrange–Assert–Act–Assert–Act–Assert… pattern, where a variety of actions followed by state or behavioural checks validate a workflow. This seems reasonable until the test fails. Any of the actions could have caused the failure, so the test doesn’t focus on testing a particular behaviour. Or it could have been the setup in Arrange that caused the error. This nested assert pattern leads to tests that are difficult to debug and maintain. Sticking to * Given-When-Then* or Arrange-Act-Assert keeps the test focused and makes it more maintainable.

Let’s apply this structure to one of our first tests as an example:

def test_equality_fail():
    # Given two item objects with known contents
    i1 = Item("do something", "veit")
    i2 = Item("do something else", "veit.schiele")
    # WHEN the two item objects are not identical
    if i1 != i2:
        # THEN the result will be a string
        pytest.fail("The items are not identical!")

The structure helps you to organise the test functions and focus on testing one behaviour. The structure also helps you to think of other test cases. Focusing on an initial state helps you to think of other states that might be relevant for testing the same action. Similarly, focusing on an ideal outcome helps you think of other possible outcomes, such as failure states or error states, that should also be tested with other test cases.

Grouping tests with classes

Up to now, we have written test functions within test modules in a file system directory. This structuring of the test code actually works quite well and is sufficient for many projects. However, pytest also allows us to group tests with classes. Let’s take some of the test functions that relate to the equality of items and group them into a class:

class TestEquality:
    def test_equality(self):
        i1 = Item("do something", "veit", "todo", 42)
        i2 = Item("do something", "veit", "todo", 42)
        assert i1 == i2

    def test_equality_with_diff_ids(self):
        i1 = Item("do something", "veit", "todo", 42)
        i2 = Item("do something", "veit", "todo", 43)
        assert i1 == i2

    def test_inequality(self):
        i1 = Item("do something", "veit", "todo", 42)
        i2 = Item("do something else", "veit", "done", 42)
        assert i1 != i2

The code looks pretty much the same as before, with the exception that each method must have an initial self argument. We can now execute all these methods together by specifying the class:

$ pytest -v tests/test_classes.py::TestEquality
============================= test session starts ==============================
…
collected 3 items

tests/test_classes.py::TestEquality::test_equality PASSED                [ 33%]
tests/test_classes.py::TestEquality::test_equality_with_diff_ids PASSED  [ 66%]
tests/test_classes.py::TestEquality::test_inequality PASSED              [100%]

============================== 3 passed in 0.00s ===============================

However, we can still call a single method:

$ pytest -v tests/test_classes.py::TestEquality::test_equality
============================= test session starts ==============================
…
collected 1 item

tests/test_classes.py::TestEquality::test_equality PASSED                [100%]

============================== 1 passed in 0.00s ===============================

If you are familiar with Object Orientation and class inheritance, you can use hierarchies of test classes for inherited helper methods. I recommend that you use test classes sparingly and mainly for grouping, even in productive test code. If you go to too much trouble with test class inheritance, it will get confusing in the future.

Executing a subset of tests

In the previous section, we used test classes to execute a subset of tests. Executing a small group of tests is very handy when debugging, or if you want to limit the tests to a specific section of the codebase you are working on. pytest allows you to execute a subset of tests in different ways:

Subset	Syntax
All tests in one directory	pytest path
All tests in a module	pytest path/test_module.py
All tests in a class	pytest path/test_module.py::TestClass
Single test function	pytest path/test_module.py::test_function
Single test method	pytest path/test_module.py::TestClass::test_method
Tests that correspond to a name pattern	pytest -k pattern
Tests by marker	siehe Markers

Whether pytest finds your test code depends on the naming:

• Test files should be named test_something.py or something_test.py.

• Test methods and functions should be named test_something.

• Test classes should be named TestSomething.

Tip

Use a directory structure that corresponds to the way you want to run your code, because it is easy to run a complete subdirectory. This way you can divide features and functions or use subsystems as a basis or orientate yourself on the code structure.

You can also use -k pattern to filter directories, classes or test prefixes, for example all tests of class TestEquality.

$ pytest -v -k TestEquality
============================= test session starts ==============================
…
collected 7 items / 4 deselected / 3 selected

test_classes.py::TestEquality::test_equality PASSED                      [ 33%]
test_classes.py::TestEquality::test_equality_with_diff_ids PASSED        [ 66%]
test_classes.py::TestEquality::test_inequality PASSED                    [100%]

======================= 3 passed, 4 deselected in 0.00s ========================

or all tests with equality in the name:

pytest -v --tb=no -k equality
============================= test session starts ==============================
…
collected 7 items / 3 deselected / 4 selected

test_classes.py::TestEquality::test_equality PASSED                      [ 25%]
test_classes.py::TestEquality::test_equality_with_diff_ids PASSED        [ 50%]
test_classes.py::TestEquality::test_inequality PASSED                    [ 75%]
test_item_fail.py::test_equality_fail FAILED                             [100%]

=========================== short test summary info ============================
FAILED test_item_fail.py::test_equality_fail - Failed: The items are not identical!
================== 1 failed, 3 passed, 3 deselected in 0.01s ===================

Unfortunately, one of these is our error example. We can remove it by expanding the expression:

$ pytest -v --tb=no -k "equality and not equality_fail"
============================= test session starts ==============================
…
collected 7 items / 4 deselected / 3 selected

test_classes.py::TestEquality::test_equality PASSED                      [ 33%]
test_classes.py::TestEquality::test_equality_with_diff_ids PASSED        [ 66%]
test_classes.py::TestEquality::test_inequality PASSED                    [100%]

======================= 3 passed, 4 deselected in 0.00s ========================

The keywords and, not, or and () are allowed to create complex expressions. Here is a test run of all tests with or “ids” in the name, but not in the “TestEquality” class:

$ pytest -v --tb=no -k "(inequality or id) and not _fail"
============================= test session starts ==============================
…
collected 7 items / 4 deselected / 3 selected

test_classes.py::TestEquality::test_equality_with_diff_ids PASSED        [ 33%]
test_classes.py::TestEquality::test_inequality PASSED                    [ 66%]
test_helper.py::test_ident PASSED                                        [100%]

======================= 3 passed, 4 deselected in 0.00s ========================

The -k keyword option, together with and, not and or, offers great flexibility when selecting the tests you want to run. This proves to be very helpful when troubleshooting or developing new tests.

Tip

It is a good idea to use quotation marks when selecting a test to run as the hyphens, brackets and spaces can confuse the shells.