Automation QA with Selenium + Pytest in Python: A Comprehensive Guide

The Power of Selenium + Pytest

Selenium is a robust tool for automating web browsers, and Pytest is a flexible and powerful testing framework in Python. Together, they form a potent duo for automated testing, enabling us to create comprehensive and reliable test suites. Selenium interacts with web browsers, allowing us to simulate user actions and verify the behavior of web applications. Pytest, on the other hand, provides a rich set of features for writing and managing tests, including fixtures, parameterized testing, and extensive plugin support.

Our Tech Stack

Our automation framework is built on the following technologies:

• Python 3.10: The programming language used for writing tests.
• Selenium: For UI automation through Chrome and Firefox web drivers.
• Pytest: For creating and managing test cases.
• Percy: A visual testing tool that captures and compares screenshots of the UI.
• Allure Report: A reporting tool that generates detailed test reports.
• Gitlab CI: A continuous integration system that automates test execution.

Each of these tools plays a crucial role in our testing process, ensuring that our tests are robust, reliable, and easy to manage.

Writing and Managing Tests

At ARTJOKER, we follow a structured approach to writing tests:

1. Understanding the Project: We start by familiarizing ourselves with the project requirements and test cases provided by the QA team.
2. Estimating Effort: We estimate the time required to write and execute the tests.
3. Setting Up: We deploy a new test project template with all necessary base classes and libraries.
4. Writing Tests: We write test cases, leveraging our predefined templates and classes to ensure consistency and ease of maintenance.
5. Executing and Reporting: We run the tests, analyze the results, and submit the final reports.

Test Execution and Reporting

Our tests are stored in dedicated repositories and executed using CI/CD pipelines. This setup allows for seamless integration and continuous testing across different environments (dev, stage, prod). When tests are triggered, they run in Docker containers, ensuring a consistent and isolated environment. Pytest handles the test execution, with each test being retried up to two times in case of failure. Allure Reports capture detailed information about each test run, providing insights into test performance and failures.

Percy runs alongside the tests, capturing screenshots for visual comparison. This helps us identify UI discrepancies that might not be apparent through traditional testing methods.

Advantages and Challenges of Automated Testing

Pros:

• Efficiency: Automated tests run faster and with fewer errors than manual tests.
• Accuracy: Repeated test runs ensure consistent and reliable results.
• Coverage: Automated tests can cover a broader range of scenarios, including those difficult to test manually.
• Collaboration: Developers can run tests independently, freeing up testers for more complex tasks.

Cons:

• Initial Investment: Developing and maintaining automated tests require upfront effort and costs.
• Limitations: Automated tests cannot cover every possible scenario and may miss edge cases that manual testing could catch.

Use Cases for Automation

Our automation framework addresses various testing needs:

• Email Automation: Automates email interactions, speeding up regression tests.
• Visual Testing: Uses Percy to capture and compare UI screenshots, ensuring layout accuracy.
• UI Testing: Verifies the functionality and appearance of user interfaces.
• Performance, Accessibility, and SEO: Uses Google Lighthouse to assess these aspects.
• Load Testing: Employs the Locust framework to simulate backend loads.
• Cross-browser Testing: Runs tests on Chrome and Firefox simultaneously.
• Security Testing: Incorporates OWASP ZAP for security assessments.
• User Story Automation: Ensures consistent execution of user flows and scenarios.