Python Unit Testing Best Practices For Building Reliable Applications

Posted on 2023-12-06 Edited on 2024-05-09 In Introduction Word count in article: 3.2k Reading time ≈ 11 mins.

In software development, the importance of unit testing cannot be overstated.

A poorly constructed test suite can lead to a fragile codebase, hidden bugs, and hours wasted in debugging.

Not to mention hard to maintain, extend, redundant, non-deterministic and no proper coverage. So how do you deal with the complexities of crafting effective unit tests?

How do you ensure your code works as expected? What’s the difference between good and great software?

The answer? Thoughtful and efficient tests.

After conversations with Senior Python developers, reading countless books on the subject and my broad experience as a Python developer for 8+ years, I collected the below Python unit testing best practices.

This article will guide you through the labyrinth of best practices and strategies to ensure your tests are reliable, maintainable and efficient.

You’ll learn how to keep your tests fast, simple, and focused on singular functions or components.

We’ll delve into the art of making tests readable and deterministic, ensuring they are an integrated part of your build process.

The nuances of naming conventions, isolation of components, and the avoidance of implementation coupling will be dissected.

We’ll also touch upon advanced concepts like Dependency Injection, Test Coverage Analysis, and Test-Driven Development (TDD).

Let’s embark on this journey to ensure your unit tests are as robust and effective as your code.

Why Care About Unit Testing Best Practices?

Without proper practices, tests can become a source of frustration and inefficiency bottlenecks.

If you don’t take the time to learn and put in place best practices from the get-go, refactoring and maintenance become difficult.

From minor issues like changing your tests often to larger issues like broken CI Pipelines blocking releases.

Common issues include

Inefficient Testing: Tests that are slow or complex can delay development cycles, making the process cumbersome and less productive.
Lack of Clarity: Badly written tests with unclear intentions make it difficult for developers to understand what is being tested or why a test fails.
Fragile Tests: Tests that are tightly coupled with implementation details can break easily with minor code changes, leading to increased maintenance.
Test Redundancy: Writing tests that duplicate implementation logic can lead to bloated test suites.
Non-Deterministic Tests: Flaky tests that produce inconsistent results undermine trust in the testing suite and can mask real issues.
Inadequate Coverage: Missing key scenarios, especially edge cases, can leave significant gaps in the test suite, allowing bugs to go undetected.

In this article, we aim to resolve these challenges by providing guidelines and techniques to write tests that are fast, simple, readable, deterministic and well-integrated into the development process.

The goal is to enhance the effectiveness of the testing process, leading to more reliable and maintainable Python applications.

Tests Should Be Fast

Fast tests foster regular testing, which in turn speeds up bug discovery. Long-running tests can slow down development and discourage regular testing.

Certain tests, due to their reliance on complex data structures, may take longer. It’s wise to divide these into a separate suite, run via scheduled tasks or use mocking to reduce external resource dependency.

Fast tests can be run regularly, a key aspect in continuous integration environments where tests may accompany every commit.

To maintain a strong development workflow, prioritize testing concise code segments and minimize dependencies and large setups.

The faster your tests, the more dynamic and efficient your development cycle will be.

Tests Should Be Independent

Each test should be able to run individually and as part of the test suite, irrespective of the sequence in which it’s executed.

Adhering to this rule necessitates that each test starts with a fresh dataset and often requires some form of post-test cleanup.

Pytest manages this process via Fixture Setup/Teardown, ensuring that each test is self-contained and its outcome unaffected by preceding or later tests.

import pytest  
  
@pytest.fixture  
def fresh_dataset():  
    # Setup a fresh dataset  
    dataset = create_dataset()  
    yield dataset  
    # Cleanup after the test  
    dataset.cleanup()  
  
def test_sample(fresh_dataset):  
    # Test using the fresh dataset  
    assert some_condition(fresh_dataset) == expected_result

Each Test Should Test One Thing

A test unit should concentrate on one small functionality and verify its correctness.

Avoid combining many functionalities in a single test, as this can obscure the source of errors and be difficult to refactor.

Instead, write separate tests for each functionality, which aids in pinpointing specific issues and ensures clarity.

This approach enhances test effectiveness and maintainability.

What Not To Do:

# Bad practice: Testing multiple functionalities in one test  
def test_user_creation_and_email_sending():  
    user = createUser("test@example.com")  
    assert user.email == "test@example.com"  
    sendWelcomeEmail(user)  
    assert emailService.last_sent_email == "test@example.com"

What To Do:

# Good practice: Testing one functionality per test  
def test_user_creation():  
    user = createUser("test@example.com")  
    assert user.email == "test@example.com"  
  
def test_welcome_email_sending():  
    user = User("test@example.com")  
    sendWelcomeEmail(user)  
    assert emailService.last_sent_email == "test@example.com"

Tests Should Be Readable (Use Sound Naming Conventions)

Readable tests act as documentation, conveying the intended behaviour of the code.

Well-structured and comprehensible tests help with easier maintenance and quicker debugging.

The use of descriptive names and straightforward logic ensures that anyone reviewing the code can understand the test’s purpose and function.

What Not To Do:

1 2	def test1(): assert len([]) == 0

What To Do:

1 2	def test_empty_list_has_zero_length(): assert len([]) == 0

Tests Should Be Deterministic

Deterministic tests give consistent results under the same conditions, ensuring test predictability and trustworthiness.

Deterministic

1 2	def test_addition(): assert 2 + 2 == 4

This test is deterministic because it produces the same outcome.

It avoids relying on external factors like network dependencies or random data, which can lead to erratic test results.

By ensuring tests are deterministic, you create a robust foundation for your test suite.

This allows for repeatable, reliable testing across different environments and over time.

Non-Deterministic

import random  
  
def test_random_number_is_even():  
    assert random.randint(1, 100) % 2 == 0

This test is non-deterministic because it relies on a random number generator.

The outcome can vary between runs, sometimes passing and sometimes failing, making it unreliable.

Managing Non-Deterministic Tests:

To manage non-deterministic tests, it’s crucial to drop randomness or external dependencies where possible.

If randomness is essential, consider using fixed seeds for random number generators.

For external dependencies like databases or APIs, use mock objects or fixtures to simulate predictable responses.

Libraries like Faker and Hypothesis allow you to test your code against a variety of sample input data.

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def test_random_number_is_even():  
    random.seed(42)  # Setting a fixed seed  
    assert random.randint(1, 100) % 2 == 0

Tests Should Not Include Implementation Details

Tests should verify behaviour, not implementation details.

They should focus on what the code does, not how it does it.

This distinction is crucial for creating robust, maintainable tests that remain valid even when the underlying implementation changes.

What Not To Do:

1 2	def test_add(): assert 2 + 3 == 5 # includes implementation detail

What To Do:

1 2	def test_add(): assert add(2, 3) == 5 # Implementation detail abstracted

In the second example, the test checks if add correctly computes the sum, independent of how the function achieves this.

This approach allows the add function to evolve (e.g., optimization, refactoring) without requiring changes to the test as long as it fulfils its intended behaviour.

Tests Should Be Part Of The Commit and Build Process

Integrating tests into the commit and build process is essential for maintaining code quality.

This integration ensures that tests are automatically run during the build, catching issues early and preventing bugs from reaching production.

It enforces a standard of code health and functionality, serving as a crucial checkpoint before deployment.

Automated testing within the build process streamlines development, enhances reliability and fosters a culture of continuous integration and delivery.

You can use pre-commit hooks to run tests before each commit, ensuring that only passing code is committed.

You can set up Pytest to run with CI tooling like GitHub Actions, Bamboo, CircleCI, Jenkins and so on that runs your test after deployment thus helping your release with confidence.

Use Single Assert Per Test Method

Each test should aim to verify a single aspect of the code, making it easier to identify the cause of any failures.

This approach simplifies tests and makes them more maintainable.

Multiple Asserts (Not Recommended):

def test_user_profile():  
    user = UserProfile('John', 'Doe')  
    assert user.first_name == 'John'  
    assert user.last_name == 'Doe'  
    assert user.full_name() == 'John Doe'

Single Assert per Test (Recommended):

def test_user_first_name():  
    user = UserProfile('John', 'Doe')  
    assert user.first_name == 'John'  
  
def test_user_last_name():  
    user = UserProfile('John', 'Doe')  
    assert user.last_name == 'Doe'  
  
def test_user_full_name():  
    user = UserProfile('John', 'Doe')  
    assert user.full_name() == 'John Doe'

In the recommended approach, each test focuses on a single assertion, providing a clear and specific test for each aspect of the user profile.

This makes it easier to diagnose issues when a test fails, as each test is focused on one functionality.

Use Fake Data/Databases in Testing

Using fake data or databases in testing allows you to simulate various scenarios without relying on actual databases, which can be unpredictable and slow down tests.

Fake data ensures consistency in test environments and prevents tests from failing due to external data changes.

It also helps keep tests faster and prevents accidental updates to the source data.

def test_calculate_average_age():  
    fake_data = [{'age': 25}, {'age': 35}, {'age': 45}]  
    average_age = calculate_average_age(fake_data)  
    assert average_age == 35

from unittest.mock import MagicMock  
  
def test_user_creation():  
    db = MagicMock()  
    db.insert_user.return_value = True  
    user_service = UserService(db)  
    result = user_service.create_user("John Doe", "john@example.com")  
    assert result is True

In the first example, fake_data is used to test the calculation function without needing real data.

In the second, a mock database (MagicMock) is used to simulate database interactions.

This technique isolates the tests from external data sources and makes them more predictable and faster.

You can also spin up a local SQLite database for your tests.

SQLite databases are lightweight, don’t need a separate server, and can be integrated into a test setup.

Use Dependency Injection

In Python unit testing, Dependency Injection (DI) is a best practice that enhances code testability and modularity.

DI involves providing objects with their dependencies from outside, rather than having them create dependencies internally.

This method is particularly valuable in testing, as it allows for easy substituting of real dependencies with mocks or stubs.

For instance, a class that requires a database connection can be injected with a mock database during testing, isolating the class’s functionality from external systems.

This separation fosters cleaner, more maintainable code and simplifies the creation of focused reliable unit tests.

Without Dependency Injection

class DatabaseConnector:  
    def connect(self):  
        # Code to connect to a database  
        return "Database connection established"  
  
class DataManager:  
    def __init__(self):  
        self.db = DatabaseConnector()  
  
    def data_operation(self):  
        return self.db.connect() + " - Data operation done"

In this scenario, testing DataManager‘s data_operation method is challenging because it’s tightly coupled with DatabaseConnector.

With Dependency Injection (DI)

class DatabaseConnector:  
    def connect(self):  
        # Code to connect to a database  
        return "Database connection established"  
  
class DataManager:  
    def __init__(self, db_connector):  
        self.db = db_connector  
  
    def data_operation(self):  
        return self.db.connect() + " - Data operation done"  
  
# During testing  
class MockDatabaseConnector:  
    def connect(self):  
        return "Mock database connection"  
  
# Test  
def test_data_operation():  
    mock_db = MockDatabaseConnector()  
    data_manager = DataManager(mock_db)  
    result = data_manager.data_operation()  
    assert result == "Mock database connection - Data operation done"

In the DI example, DataManager receives its DatabaseConnector dependency from the outside.

This allows us to inject a MockDatabaseConnector during testing, focusing the test on DataManager‘s behaviour without relying on the actual database connection logic.

This makes the test more reliable, faster, and independent of external factors like a real database.

Use Setup and Teardown To Isolate Test Dependencies

In Python unit testing, isolating test dependencies is key to reliable outcomes.

Setup and teardown methods are instrumental and enable reusable and isolated test environments

The setup method prepares the test environment before each test, ensuring a clean state.

Conversely, the teardown method cleans up after tests, removing any data or state alterations.

This process prevents interference between tests, maintaining their independence.

Fixtures, in Pytest, provide a flexible way to set up and tear down code for many tests.

import pytest  
  
@pytest.fixture  
def resource_setup():  
    # Setup code: Create resource  
    resource = "some resource"  
    print("Resource created")  
      
    # This yield statement is where the test execution will take place  
    yield resource  
  
    # Teardown code: Release resource  
    print("Resource released")  
  
def test_example(resource_setup):  
    assert resource_setup == "some resource"

This approach ensures that the resource is set up before each test that uses the fixture and properly cleaned up afterwards, maintaining test isolation and reliability.

If you need a refresher on Pytest fixture setup and teardown, this article is a good starting point.

You can easily control fixture scopes using the scope parameter as detailed in this guide.

Organizing related tests into modules or classes is a cornerstone of good unit testing in Python.

This practice enhances test clarity and manageability.

By grouping, you logically categorize tests based on functionality, such as testing a specific module or feature.

This not only makes it easier to locate and run related tests but also improves the readability of your test suite.

For instance, all tests related to a User class might reside in a TestUser class:

class TestUser(unittest.TestCase):  
    def test_username(self):  
        # Test for username  
    def test_password(self):  
        # Test for password

This structure ensures an organized approach, where related tests are grouped, making it easier to navigate and maintain the test suite.

Mock Where Necessary (with Caution)

In Python unit testing, the judicious use of mocking is essential.

Mocking replaces parts of your system under test with mock objects, isolating tests from external dependencies like databases or APIs.

This accelerates testing and focuses on the unit’s functionality.

However, over-mocking can lead to tests that pass despite bugs in production code, as they might not accurately represent real-world interactions.

These articles provide a practical guide on how to use Mock and Monkeypatch in Pytest.

Use Test Framework Features To Your Advantage

Leveraging the full spectrum of features offered by Python test frameworks like Pytest can significantly enhance your testing practice. Key among these in Pytest are conftest.py, fixtures, and parameterization.

conftest.py serves as a central place for fixture definitions, making them accessible across multiple test files, thereby promoting reuse and reducing code duplication.

Pytest Fixtures offer a powerful way to set up and tear down resources needed for tests, ensuring isolation and reliability.

Pytest Parameterization allows for the easy creation of multiple test cases from a single test function, enabling efficient testing of various input scenarios.

Harnessing these features streamlines the testing process, ensuring more organized, maintainable, and comprehensive test suites.

Practice Test Driven Development (TDD)

TDD is a foundational practice involving writing tests before writing the actual code.

Start by creating a test for a new function or feature, which initially fails (as the feature isn’t implemented yet).

Then, write the minimal amount of code required to pass the test.

This cycle of “Red-Green-Refactor” — writing a failing test, making it pass, and then refactoring — guides development, ensuring that your codebase is thoroughly tested and designed with testing in mind.

As you iterate, the tests evolve along with the code, facilitating a robust and maintainable codebase.

Regularly Review and Refactor Tests

Consistent review and refactoring of tests are crucial.

This involves examining tests to ensure they remain relevant, efficient, and readable.

Refactoring might include removing redundancy, updating tests to reflect code changes, or improving test organization and naming.

This proactive approach keeps the test suite streamlined, improves its reliability, and ensures it continues to support ongoing development and codebase evolution.

Analyze Test Coverage

Coverage analysis involves using tools like coverage.py to measure the extent to which your test suite exercises your codebase.

High coverage means you’ve tested a larger part of your code.

While this is helpful, it doesn’t mean your code is free from bugs. It means you’ve tested the implemented logic.

Testing edge cases and against a variety of test data to uncover potential bugs should be your top priority.

You can read more about how to generate Pytest coverage reports here.

Test for Security Issues as Part of Your Unit Tests

Security testing involves crafting tests to uncover vulnerabilities like injection flaws, broken authentication, and data exposure.

Using libraries like Bandit, you can automate the detection of common security issues.

For instance, if your application involves user input processing, your tests should include cases that check for common security issues like SQL injection or cross-site scripting (XSS).

Consider a function that queries a database:

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def get_user_details(user_id):  
    query = f"SELECT * FROM users WHERE id = {user_id}"  
    # Execute query and return results

This function is vulnerable to SQL injection. To test for this security issue, you can write a unit test that attempts to exploit this vulnerability:

import pytest  
from mymodule import get_user_details  
  
def test_sql_injection_vulnerability():  
    malicious_input = "1; DROP TABLE users"  
    with pytest.raises(SecurityException):  
        get_user_details(malicious_input)

In this test, malicious_input simulates an SQL injection attack.

The test checks if the function get_user_details properly handles such input, ideally by raising a SecurityException or similar.

Using tools like Bandit, you can automatically scan your code for such vulnerabilities and known security issues.

I would also recommend against using raw SQL in your code unless absolutely necessary and instead use an ORM (Object Relational Mapping) tool like SQLAlchemy or SQL Model to perform database operations.

Conclusion

In this comprehensive guide, we’ve gone through the essential Python unit testing best practices.

From the importance of fast, independent, and focused tests, to the clarity that comes from readable and deterministic tests, you now have the tools to enhance your testing strategy.

We’ve covered advanced techniques like Dependency Injection, Test Coverage Analysis, Test-Driven Development (TDD), and the critical role of security testing in unit tests.

As you apply these best practices, continue to review and refine your tests.

Embrace the mindset of continuous improvement and let these principles guide you towards creating exemplary Python applications.

Your journey in mastering Python unit testing doesn’t end here; it evolves with every line of code you write and the tests you create.

So, go ahead, apply these Python unit testing best practices, and witness the transformation in your development workflow. Happy testing! 🚀🐍🧪

If you have ideas for improvement or like me to cover anything specific, please send me a message via Twitter, GitHub or Email.

Additional Reading

What is Setup and Teardown in Pytest? (Importance of a Clean Test Environment)
How To Generate Beautiful & Comprehensive Pytest Code Coverage Reports (With Example)
Introduction to Pytest Mocking - What It Is and Why You Need It
The Ultimate Guide To Using Pytest Monkeypatch with 2 Code Examples
A Step-by-Step Guide To Using Pytest Fixtures With Arguments
How to Use Hypothesis and Pytest for Robust Property-Based Testing in Python
Automated Python Unit Testing Made Easy with Pytest and GitHub Actions
What Are Pytest Fixture Scopes? (How To Choose The Best Scope For Your Test)
Testing Your Code - The Hitchhiker’s Guide to Python
Python Unit Testing: Best Practices to Follow
Unit Testing Best Practices: 9 to Ensure You Do It Right
Python’s top testing best practices

Why Care About Unit Testing Best Practices?

Tests Should Be Fast

Tests Should Be Independent

Each Test Should Test One Thing

Tests Should Be Readable (Use Sound Naming Conventions)

Tests Should Be Deterministic

Tests Should Not Include Implementation Details

Tests Should Be Part Of The Commit and Build Process

Use Single Assert Per Test Method

Use Fake Data/Databases in Testing

Use Dependency Injection

Use Setup and Teardown To Isolate Test Dependencies

Group Related Tests

Mock Where Necessary (with Caution)

Use Test Framework Features To Your Advantage

Practice Test Driven Development (TDD)

Regularly Review and Refactor Tests

Analyze Test Coverage

Test for Security Issues as Part of Your Unit Tests

Conclusion

Additional Reading