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Case Study 05: Test Automation for Dynamic Content Changes in eCommerce Platforms

Background: In today's fast-moving eCommerce world, it's crucial to keep the user experience smooth and hassle-free. Online retailers frequently update their content—product listings, prices, promotional banners, and more—to stay competitive and cater to customer preferences. However, these frequent updates pose a significant challenge for the Software Testing team. Therefore, addressing the Frequent Updates is a challenge. How to handle the frequent updates during a short time was the key challenge. To overcome this, the team mainly focused on ensuring that automated test scripts remain relevant and up-to-date amidst these dynamic changes is essential for maintaining website functionality.

Strategy: Before preparing the automation test suite, the team had discussions with the stakeholders to identify the dynamic content changes. Then the team focused on the below areas of the eCommerce website.`

  • Product Listings:

-> Testing dynamic product displays, sorting, filtering, and pagination.

-> Verifying the correct product information, such as prices, descriptions, and images.

  • Search Functionality:

-> Ensuring search results are accurate and relevant.

-> Testing search filters.

  • Promotions and Discounts:

-> Verifying application of promotional codes and discounts.

-> Ensuring dynamic pricing rules are applied correctly.

  • Inventory Management:

-> Checking stock levels and availability updates.

-> Ensuring out-of-stock items are handled appropriately in the UI.

Challenges & Solutions: Implementing automated test scripts for dynamic content changes involves several challenges. It's all about maintenance. Based on the study, 30% of testers' time goes to test maintenance.

Isn't it crazy? :D + Nightmare

  • Identify the suitable Locators: The selection of locators was an important part. Because if particular attribute was changed, then it requires the changes of locators in the test script. In the selection of dynamic locators also, the team had to consider about complexity of the locators and the performance aspects of the application. Because writing and maintaining dynamic locators can be more complex than using simple, static locators. It requires a deeper understanding of the application's structure and more advanced knowledge of locator strategies.

The other thing is dynamic locators, especially those using complex XPath expressions or involving multiple attribute checks, can be slower to execute compared to simpler, direct locators.

However, unlike static locators, which may break when an element's attributes change, dynamic locators are more flexible and resilient. To address the drawbacks of applying dynamic locators such as Performance overhead and unintended elements, the team got the Dev team's support. The team got the Dev support in this matter and both agreed to keep the structure with minimal attribute changes by going with a unique attributes structure. Apart from that, the team discussed with the Dev team before implementing the changes and those discussions were held to identify the impact of the changes. It went to the extent of identifying the impact of automation test scripts. It helped with script troubleshooting when analyzing the failures, too.

Next time we would like to go with AI-based dynamic locators. :)

  • Test Data Maintenance: High test data maintenance effort was required to update test scripts in line with content changes. As an example when there were changes to the product list, then it affects the search functionality, discounts, inventory management and so on. Implementing data-driven approaches where test data is externalized from the test scripts, allowing for easy updates. Store test data in external files such as CSV, making it easier to update without altering the test scripts.

  • Modular Test Design: Creating modular and reusable test components to reduce redundancy and enhance maintainability was the other key thing the team did as a best practice. The team adhered to the Page Object Model (POM) by creating separate classes for each page of the application. Apart from that, the team managed well-organized test suites to run relevant tests based on changes (e.g., smoke tests, regression tests).

Adopting these strategies and solutions led to:

  • Reduced Test Maintenance Effort: Automated adaptation of test scripts with minimal maintenance effort significantly reduces the manual effort, and troubleshooting required for maintenance.

  • Enhanced Test Coverage: Ensures comprehensive testing across all dynamic content changes, leading to better test coverage. Finally, it ensures the flawless application.

  • Faster Time-to-Market: Streamlined testing processes contribute to faster releases and updates, giving eCommerce platforms a competitive edge.

  • Cost Efficiency: Lower maintenance costs and efficient use of resources result in overall cost savings.

Conclusion: In the ever-evolving landscape of eCommerce, the ability to adapt automated test scripts to dynamic content changes is crucial. By employing dynamic locators, data-driven testing, and modular test design, the Test teams can ensure their testing processes are robust, reliable, and efficient. These strategies not only enhance the accuracy and coverage of tests but also contribute to faster and more cost-effective product releases. As eCommerce platforms continue to grow and evolve, the adoption of adaptive automated testing will be key to maintaining a high-quality user experience.

By implementing these best practices, the eCommerce platform can stay ahead of the curve, ensuring that dynamic content changes do not compromise the quality and functionality of your site.

Some questions for you regarding AI-based dynamic locators

1. Have you used AI-based dynamic locators? If so, what is the tool?

2. Does it target the right element?

3. Does it facilitate to highlight the attribute changes?

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