Python Unit Testing

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Unit Testing is a software testing method where individual units of source code (such as functions, methods, or classes) are tested to determine whether they are working correctly.

"But my code works now!" Maybe it works now, but what if 6 months later you change one line of code and break other features? Unit tests are your safety net.

In Python, there are two main testing frameworks: unittest (built-in) and pytest (third-party, but very popular).

1. Using `unittest` (Built-in)

unittest is inspired by JUnit (Java). It uses Class-based approach.

Suppose we have a simple function:

# calc.py
def add(x, y):
    return x + y

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

We create the test file:

# test_calc.py
import unittest
from calc import add, divide

class TestCalc(unittest.TestCase):
    
    def test_add(self):
        self.assertEqual(add(3, 4), 7)
        self.assertEqual(add(-1, 1), 0)
        
    def test_divide(self):
        self.assertEqual(divide(10, 2), 5)
        
        # Test exception
        with self.assertRaises(ValueError):
            divide(10, 0)

if __name__ == '__main__':
    unittest.main()

Run with: python test_calc.py

2. Using `pytest` (Modern Recommendation)

pytest is much more concise, powerful, and "Pythonic". It uses regular functions (not classes) and standard assert keyword.

Installation:

pip install pytest

Writing tests with pytest:

# test_calc_pytest.py
import pytest
from calc import add, divide

def test_add():
    assert add(3, 4) == 7
    assert add(-1, 1) == 0

def test_divide():
    assert divide(10, 2) == 5

def test_divide_zero():
    with pytest.raises(ValueError):
        divide(10, 0)

Run simply by typing: pytest in terminal. Pytest will automatically look for files starting with test_.

3. Mocking Concept

Mocking is a technique of replacing parts of the system being tested with mock objects. This is useful when your code depends on external systems like API, Database, or File System.

Example: We want to test a function that makes an API request, but we don't want to actually make the request (because it's slow and needs internet).

Using unittest.mock:

from unittest.mock import Mock, patch
import requests

# Function to test
def get_user_data(url):
    resp = requests.get(url)
    if resp.status_code == 200:
        return resp.json()
    return None

# Test with Mock
@patch('requests.get')
def test_get_user_data(mock_get):
    # Setup mock
    mock_response = Mock()
    mock_response.status_code = 200
    mock_response.json.return_value = {"id": 1, "name": "Budi"}
    
    # Put mock response into mock_get
    mock_get.return_value = mock_response
    
    # Run function
    result = get_user_data("http://fakeurl.com")
    
    # Assert
    assert result["name"] == "Budi"
    # Ensure requests.get is actually called with correct URL
    mock_get.assert_called_with("http://fakeurl.com")

4. Code Coverage

How much of your code is tested? Coverage tools can tell you which lines have not been touched by tests.

Install:

pip install pytest-cov

Run:

pytest --cov=my_project

Conclusion

Unit Test is mandatory for serious applications.
pytest is preferred because of its clean syntax.
Mocking is used to isolate units from external dependencies.
Get used to writing tests before code (TDD - Test Driven Development) or at least along with the code.

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