pytest's Mocker fixture is a powerful tool for mocking and patching during testing, providing immense flexibility in controlling the behavior of your code. However, handling multiple exceptions gracefully can sometimes feel tricky. This guide will equip you with the strategies to effectively manage various exceptions when using pytest-mock's Mocker fixture, transforming complex exception handling into a streamlined process.
Understanding the Challenge: Multiple Exceptions
When testing code that might raise multiple types of exceptions under different conditions, simply mocking the exception isn't enough. You need a more sophisticated approach to verify that your code reacts correctly to each specific exception. Ignoring certain exceptions or treating them all the same can lead to subtle bugs that go undetected until much later.
Mastering Mocker's Exception Handling Capabilities
The core of our solution lies in the side_effect parameter of mocker.patch. Instead of setting side_effect to a single exception, you can provide a list of exceptions or a function that dynamically returns exceptions based on specific inputs.
Using a List of Exceptions
This is useful when you have a pre-defined sequence of exceptions you want to simulate:
import pytest
def my_function(x):
if x == 0:
raise ZeroDivisionError("Cannot divide by zero")
elif x < 0:
raise ValueError("Input must be non-negative")
return x * 2
def test_my_function(mocker):
mocked_function = mocker.patch("my_module.my_function", side_effect=[ZeroDivisionError("Cannot divide by zero"), ValueError("Input must be non-negative"), 4]) # Replace my_module with your module
assert mocked_function.call_count == 0
with pytest.raises(ZeroDivisionError) as excinfo:
mocked_function(0)
assert str(excinfo.value) == "Cannot divide by zero"
assert mocked_function.call_count == 1
with pytest.raises(ValueError) as excinfo:
mocked_function(-1)
assert str(excinfo.value) == "Input must be non-negative"
assert mocked_function.call_count == 2
assert mocked_function(1) == 4
assert mocked_function.call_count == 3
This example demonstrates patching my_function with a list containing the expected exceptions and a valid return value. The test then explicitly asserts that the correct exceptions are raised for the specific inputs.
Using a Function as a side_effect
For more complex scenarios, define a function to generate exceptions based on the input arguments. This provides much greater flexibility:
import pytest
def my_function(x, y):
if y == 0:
raise ZeroDivisionError("Cannot divide by zero")
elif x < 0:
raise ValueError("x must be non-negative")
return x / y
def exception_generator(x,y):
if y==0:
raise ZeroDivisionError("Cannot divide by zero")
elif x<0:
raise ValueError("x must be non-negative")
return x/y
def test_my_function(mocker):
mocked_function = mocker.patch("my_module.my_function", side_effect=exception_generator) # Replace my_module with your module
with pytest.raises(ZeroDivisionError) as excinfo:
mocked_function(1,0)
assert str(excinfo.value) == "Cannot divide by zero"
with pytest.raises(ValueError) as excinfo:
mocked_function(-1,1)
assert str(excinfo.value) == "x must be non-negative"
assert mocked_function(2,1) == 2.0
This method allows for dynamic exception handling based on the arguments passed to the mocked function, making your tests more robust and comprehensive.
Handling Unexpected Exceptions
Sometimes, unexpected exceptions might arise during testing. You can use pytest.raises to explicitly check for these unexpected situations:
import pytest
def my_function(x):
if x == 0:
raise ZeroDivisionError("Cannot divide by zero")
return x * 2
def test_my_function_unexpected(mocker):
mocked_function = mocker.patch("my_module.my_function", side_effect=TypeError("Unexpected type")) # Replace my_module with your module
with pytest.raises(TypeError) as excinfo:
mocked_function(1)
assert "Unexpected type" in str(excinfo.value)
Conclusion
pytest-mock's Mocker fixture provides a powerful mechanism for comprehensive testing, enabling you to effectively manage multiple exceptions with precision and clarity. By using lists or functions as side_effect, your tests become more robust, reliable, and better at catching edge cases. Remember to always account for unexpected exceptions to ensure your tests cover all bases. This approach makes testing code that interacts with external systems or libraries significantly easier and reduces the likelihood of undetected errors.
