A Beginner's Guide To `pytest_generate_tests`

A Beginner’s Guide To pytest_generate_tests (Explained With 2 Examples)

Enter pytest-generate-tests, a powerful Pytest plugin that promises to revolutionize the way you write tests.

Introduction to pytest_generate_tests

The pytest_generate_tests hook is a powerful feature provided by the Pytest testing framework that allows you to dynamically generate test cases.

This function allows you to programmatically create test cases based on a variety of conditions, inputs, or scenarios specific to your project’s requirements.

Unlike static parametrization, where test cases are predefined, pytest_generate_tests enables you to determine parameters on-the-fly, which is especially valuable when dealing with complex or evolving codebases.

pytest_generate_tests is called for each test function in the module to give a chance to parametrize it.

Example 1 - Pytest Generate Tests

# sample code to explain how pytest_generate_tests work
 
import pytest
 
def add(a, b):
    return a + b
 
# Define the data for test case generation
test_data = [
    ((1, 2), 3),   # Input: (1, 2) | Expected Output: 3
    ((0, 0), 0),   # Input: (0, 0) | Expected Output: 0
    ((-1, 1), 0),  # Input: (-1, 1) | Expected Output: 0
]
 
# Define the pytest_generate_tests hook to generate test cases
def pytest_generate_tests(metafunc):
    if 'test_input' in metafunc.fixturenames:
        # Generate test cases based on the test_data list
        metafunc.parametrize('test_input,expected_output', test_data)
 
# Define the actual test function
def test_addition(test_input, expected_output):
    result = add(*test_input)
    assert result == expected_output, f"Expected {expected_output}, but got {result}"

Difference between parametrize and generate_tests

In short, for simple, fixed inputs, use pytest.mark.parametrize. For dynamic or intricate setups, pytest_generate_tests is the way to go.

Limitations of pytest_generate_tests

For example, when you need to generate test cases with complex logic or extensive data manipulation, pytest_generate_tests might not be the best fit.

It is more suitable for simpler cases where test parameterization is straightforward.

While using pytest_generate_tests, this nightmare may come true as debugging dynamically generated tests can be trickier, especially when dealing with a large number of test cases.