What Are Pytest Mock Assert Called Methods and How To Leverage Them

Mocking plays a crucial role in unit testing by isolating code under test from its dependencies, such as external services, APIs, or complex libraries.

This is where Pytest Mock Assert Calls come into play. It allows you to assert method calls on mock objects, ensuring that the code under test interacts correctly with its dependencies.

Basics of Pytest Mocking

The Pytest-mock plugin extends Pytest’s capabilities to include mocking (built off unittest.mock), making it easier to isolate code units and verify their interactions.

To use the pytest-mock plugin, you need to ensure it is installed in your Python environment. You can install it using pip:

Once installed, you can leverage the mocker fixture provided by pytest-mock for mocking within your tests.

The mocker fixture allows you to create mock objects, patch methods and attributes, and set return values for these mocks.

import pytestfrom unittest.mock import Mock, patchdef get_total_price(price, quantity):    return price * quantitydef test_get_total_price():    # Create a mock object    mock_calculator = Mock()    # Patch the 'get_total_price' function to use the mock_calculator    with patch(__name__ + '.get_total_price', side_effect=mock_calculator) as mocked_function:        # Set the return value for the mock_calculator        mock_calculator.return_value = 25        # Call the function under test        result = get_total_price(5, 5)        # Verify that the mock_calculator was called with the correct arguments        mocked_function.assert_called_once_with(5, 5)        # Verify the result of the function        assert result == 25  # The result is the correct total cost (5 * 5)

Pytest Mock Assert Call - The Key Concepts

Pytest-Mock provides various assertion methods, including

• assert_called

• assert_called_once

• assert_called_once_with

• assert_called_with

and others. We’ll study all these methods in detail below. You can read more about the various assert methods of the Unittest.Mock library here.

These methods allow you to verify that a method on a mock object was called with the correct arguments during a test.

assert_called

Basically, can use the assert_called method to verify that a specific method on a mock object has been called during a test. No matter how many times it’s called, it should have been atleast called once.

assert_called_once

In some situations, you might just want to call a specific function on a mock object only once. This can be done using assert_called_once.

assert_called_once_with

However, if you want to verify that a specific method on a mock object was called but also that it was called exactly once and with specific arguments, then you should use assert_called_once_with.

# Use assert_called to check if the method was called

sample_mock.some_method.assert_called_once_with(10)

assert_called_with

Let’s imagine that you don’t care how many times a specific method is called, but you want to ensure that every time the method is called, it is called with some fixed arguments. Well, then you use assert_called_with.

# Use assert_called_with to check if the method was called with the expected arguments

sample_mock.some_method.assert_called_with(10, 20)

In the above snippet, assert_called_with is used to check if the some_method was called with the expected arguments (10 and 20). If the method is called with these arguments, the assertion will pass; otherwise, it will raise an AssertionError.

assert_not_called

This assertion is valuable for ensuring that certain parts of your code were not called, helping you detect unexpected or unwanted behavior.

# Use assert_not_called to check if the method was not called

sample_mock.some_method.assert_not_called()

assert_has_calls

assert_has_calls is a useful method in Pytest-Mock for asserting the order in which methods were called on a mock object.

This can be important in scenarios where the sequence of method calls is critical to the behavior of your code.

Suppose you have a Python program for a coffee shop’s order processing system. You want to ensure that orders are processed in the correct sequence, which involves taking orders, preparing drinks, and serving them.

# Define the expected call sequence

expected_calls = [call.some_method(10), call.another_method(“hello”)]

# Use assert_has_calls to check if the calls match the expected sequence

sample_mock.assert_has_calls(expected_calls)

assert_has_calls has been used to check if the actual calls on the mock object match the expected call sequence. This means that first, some_method(10) will run, and then another_method("hello") will be executed.

assert_any_call

Now let’s study when to use assert_any_call.

Use it to verify that a specific method on a mock object has been called at least once during a test with the correct arguments.

Frequently Asked Questions

Are mock and MagicMock the Same Methods?

No, they are not the same. The Mock class in Python’s unittest.mock library is used to mock objects.

A mock object simulates the behavior of the object it replaces by creating attributes and methods on-the-fly.

In other words, you can access any attribute or method on a mock object, and if it doesn’t exist, the mock object will dynamically create it.

In contrast, the MagicMock class is an extension of the Mock class.

It has all the capabilities of a regular Mock, but with the addition of most of the “magic” or “dunder” methods pre-created. These are special methods in Python, like __getitem__ or __len__, that enable custom behaviors for common operations.

If you need a mock object to emulate special methods or operations, using MagicMock is often more convenient than using a basic Mock.

Best Practices and Tips

Naming Conventions for Mock Objects

When naming mock objects, adhere to meaningful and descriptive names that reflect the purpose of the mock.

For example, if you’re mocking a database connection, you could name it mock_db_connection. This makes your tests more readable and helps other developers understand the test’s intent.

Isolating Test Cases

Isolating test cases when using pytest-mock-assert-calls is crucial to ensure that each test runs independently without interference from previous or subsequent tests.

Keeping your Tests DRY

Don’t get it wrong, but keep your tests as DRY (Don’t Repeat Yourself) as possible.

This means writing reusable utility functions or fixtures for common setup and assertions. Use Pytest fixture functions to set up common resources, including mock objects, that can be reused across multiple test functions.

When to Use Mocking and When to Avoid It

When you want to test the actual interaction between your code and external dependencies, consider integration testing instead of mocking.

When conducting performance or load testing, you can use mocks to simulate external systems, making it easier to measure the performance of the code under test without affecting real resources.