More testing concepts

testing
Last reviewed

February 12, 2025

Last modified

March 25, 2025

Code Coverage

Code coverage measures how much of your code is executed during testing. It is a useful metric to ensure that your tests are comprehensive and indicate your code’s quality. If your software becomes a dependency for others, a code coverage of 70% or higher is recommended for unit tests.

Error handling

Tests should check if your code behaves as expected when it encounters errors. This includes testing if the code raises the correct exceptions when given invalid input or when an error occurs.

Example in Python:

def divide(x, y):
    if y == 0:
        raise ValueError("Cannot divide by zero")
    return x / y

def test_divide_by_zero():
    with pytest.raises(ValueError):
        divide(1, 0)

Fixtures

Fixtures are predefined states or sets of data used to set up the testing environment, ensuring consistent conditions for tests to run reliably. Fixtures can be used to set up databases, create temporary files, or initialize other resources, that then available to all tests in a test suite.

Parameterization

Parameterization involves running the same test with different inputs or configurations to ensure broader coverage and identify potential edge cases.

Mocking

Mocking (or monkeypatching) is a technique used to simulate the behavior of dependencies or external systems during testing, allowing isolated testing of specific components. For example, if your software requires a connection to a database, you can mock this interaction during testing.

MathWorks, MATLAB Mocking Diagram, MATLAB Documentation, link to image.

The term monkey patch seems to have come from an earlier term, guerrilla patch, which referred to changing code sneakily – and possibly incompatibly with other such patches – at runtime. The word guerrilla, nearly homophonous with gorilla, became monkey, possibly to make the patch sound less intimidating.

Marks and tags

You can use test tags to group tests into categories and then run tests with specified tags. This is useful when you want to run only a subset of tests, such as regression tests, or when ignoring slow tests during development.