Software Testing - Patterns & Practices

In the world of software development, testing is an essential process that ensures the quality and reliability of applications. Different testing patterns target various aspects of the software, from individual units to the entire system. Understanding these testing patterns can significantly enhance the robustness and reliability of your software products.

Common Testing Patterns

Unit tests

• Focus on individual components of code in isolation
• Testing e.g., function, class, module, assertion, math functions, string manipulation, data processing, file i/o, etc.

Integration tests

• Focus on interaction between different units of code
• Testing e.g., DB, API, UI, Hardware, etc.

End-to-end tests

• Focus on simulating the actions and behaviours of an end-user, from start of interaction to the end
• Testing e.g., e-commerce website checkout, banking application transactions, flight booking system, social media app, etc.

Security tests

• Focus on penetration testing, vulnerability scanning, and security code analysis.
• Testing e.g., web application security (SQL injection, cross-site-scripting, cross site request forgery), network security (open ports, weak encryption protocols, unauthorized access), mobile application security (data leakage, data storage), cloud security (misconfigured servers), etc.

Performance tests

• Focus on load testing, stress, stress testing, and endurance testing to improve user experience (slow page load times) and ensure that application can handle a harge influx of users or transactions (increased scalability)without experiencing performance degradation or failures (reduced downtime).
• Testing e.g., website performance, DB performance, network performance, server performance, etc.

Regression tests

• Focus on re-running previously executed test cases to ensure app still works correctly after changes and does not cause unexpected side effects
• Testing e.g., unit regression, GUI, integration regression, e2e regression, performance, etc.

Property-based testing is a complementary approach to traditional unit testing where test cases are generated based on properties or constraints that the code should satisfy. Hypothesis (Python lib) addresses this limitation by automatically generating test data based on specified strategies

Python unit testing best practices

Without proper practices, tests can become a source of frustration and inefficiency bottlenecks. From minor issues like changing your tests often to larger issues like broken CI Pipelines blocking releases, common issues include inefficient testing (slow/complex tests delay dev cycles), lack of clarity (what is being tested and why test fail), fragile tests (tightly coupled with implementation), 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)

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, a key aspect in continuous integration environments where tests may accompany every commit which in turn speeds up bug discovery. To maintain a strong development workflow, prioritize testing concise code segments and minimize dependencies and large setups.

• 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 (fixture setup/teardown).

• Each test should test one thing: Write separate tests for each functionality, which aids in pinpointing specific issues and ensures clarity.

# 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"

# 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: Readable tests act as documentation. The use of descriptive names and straightforward logic ensures that anyone reviewing the code can understand the test’s purpose and function.

• Tests should be deterministic: Deterministic tests give consistent results under the same conditions, ensuring test predictability and trustworthiness. 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, Model Bakery, and Hypothesis allow you to test your code against a variety of sample input data.

• Tests should not include implementation details: Tests should verify behaviour (what the code does), not implementation details (how the code does it). This distinction is crucial for creating robust, maintainable tests that remain valid even when the underlying implementation changes.

  • What not to do:

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

  • What to do:

    def test_add():
        result = add(2, 3)
        assert result == 5
    

• Tests should not be excluded from commit and build process: It enforces a standard of code health and functionality, serving as a crucial checkpoint before deployment. You can use pre-commit hooks to run tests before each commit, ensuring that only passing code is committed (pip install pre-commit). You can set up Pytest to run with CI tooling like GitHub Actions, Bamboo, CircleCI, Jenkins, etc., to run your tests after deployment, thus helping your release with confidence.

• Use fake data/db in testing: Using fake data or databases in testing ensures consistency in test environments and allows you to simulate various scenarios without relying on actual databases, which can be unpredictable and slow down tests.

• Group related tests: Organizing related tests into modules or classes is a cornerstone of good unit testing in Python. For instance, all tests related to a user might reside in a TestUser class.

• Mock where necessary (with caution): Over-mocking can lead to tests that pass despite bugs in production code, as they might not accurately represent real-world interactions.

• Use test framework features to your advantage: Key among these in Pytest are conftest.py, fixtures, and parameterization.

• Practice test driven development (TDD): The 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.

• Regularly review and refactor tests: 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 measuring 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.

• 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 (fixture setup/teardown).

• Each test should test one thing: Write separate tests for each functionality, which aids in pinpointing specific issues and ensures clarity.

Good luck testing the software you build!