How to Write the Perfect Test for Automated Software Testing

Introduction

The development world is advancing at a rapid pace, and the need to ensure code quality is more critical than ever. Even a small change in the code can lead to unexpected bugs that might disrupt the entire system. This is where automated software testing comes into play. Writing tests that do not depend on an existing environment is a challenge in itself, as dependency on a fixed environment can lead to recurring issues during the testing phase.

In this article, we will delve into the complex challenge of writing tests based on creating dynamic data. We will present the problem of environment-dependent tests and explain how to overcome them with creative solutions and advanced technologies. By the end of the article, you will understand how to write resilient tests that will ensure the best quality of your code.

Step One: Understanding the Problem in Automated Software Testing

The Problem of Relying on Existing Data

One of the most common problems in software testing is the reliance on existing data in the testing environment. This data can change for various reasons, such as system updates or changes in the configuration of the testing environment. In such cases, tests may fail even though the code itself is correct. Additionally, these tests struggle to handle environmental changes when the existing data is no longer available or suitable.

Example of an Initial Test in Software Testing

Let's examine an initial test that checks the function get_users, which returns the list of users in the system.

def test_get_users():
    user_to_find = {
        'username': 'existing_user',
        'email': 'existing_user@example.com',
        'password': 'password123'
    }
    users = get_users()
    # Check if a user with the name, email, and password exists
    user_in_list = next((user for user in users if user['name'] == 			   user_to_find['username']), None)
    assert user_in_list is not None, "No user with name 'existing_user' found"
    assert user_in_list['name'] == user_to_find['username']
    assert user_in_list['email'] == user_to_find['email']
    assert user_in_list['password'] == user_to_find['password']

In this case, the test assumes there is a user named 'existing_user' in the list of users, and as long as this user is present in the testing environment, the test will pass. But if the testing data changes, the test will fail, despite the get_users function working correctly. This indicates a problem with a test that is not entirely detached from the environment.

Step Two: Creating Data Dynamically in Automated Software Testing

The Solution: Creating Dynamic Data

To tackle this problem, the solution is to create the data needed for the test dynamically, test the functionality, and then delete the data created. This approach ensures that tests are resilient to changes in the testing environment.

Improved Example of a Test in Software Testing

Now, let's present an example where we create dynamic data for the test. Assume we have functions/services create_user and delete_user to create and delete users in the system.

def test_get_users():
    # Create data before the test
    test_user = {
        'username': 'testuser',
        'email': 'testuser@example.com',
        'password': 'password123'
    }
    create_user(test_user)
    try:
        # Perform the test
        users = get_users()
        # Find the created user in the user list
        user_in_list = next((user for user in users if user['name'] == test_user['username']), None)
        # Verify that the user was found and all attributes are correct
        assert user_in_list is not None, "No user with name 'testuser' found"
        assert user_in_list['name'] == test_user['username']
        assert user_in_list['email'] == test_user['email']
        assert user_in_list['password'] == test_user['password']
    finally:
        # Delete the data after the test
        delete_user(test_user['username'])

In this case, the test includes creating a new user before the test using the create_user function and deleting the user after the test using the delete_user function. This way, we ensure the test relies on data created specifically for the test and not on existing data that can change.

Step Three: Managing and Maintaining Tests

Data Management

It is important to manage the creation of data efficiently and ensure the data is deleted at the end of the test to prevent data contamination and future disruptions. Additionally, consider using methods like fixtures if you are using pytest or Playwright to ensure a clean testing environment. For example, the Cleanuptotal plugin optimally performs the cleanup task.

Step Four: Transition to Component and Modular Testing

Component and Modular Testing

In addition to creating dynamic data, it is recommended to focus on component tests, which test individual services or components in isolation, and modular tests, which test smaller units or modules of the system. This approach ensures that each function is tested separately, and the tests are not dependent on each other, ensuring flexibility and ease of maintenance

Summary

Writing tests is a critical part of the development process, and it is important to write them in a way that ensures their stability and reliability. By creating dynamic data before the test and deleting it after the test, you can ensure that the tests are resilient to changes in the testing environment and can continue to function correctly even in changing conditions. This approach not only improves the reliability of the tests but also allows development to proceed quickly and safely. Data management, the use of advanced techniques like fixtures, and a focus on component testing all together create a strong methodology for writing successful and efficient tests.