Scaling e-commerce testing with automated workflows

Challenge

SHOPLAB, a Swedish company, is focused on developing a sophisticated e-commerce management platform. With a streamlined development team and key stakeholders aligned under a dedicated product owner, their decision-making process is agile and effective. However, relying on external QA resources became too costly.

To reduce expenses without compromising product quality, they sought a solution to replace their external QA. Given the complexity of the platform, they needed to strike a careful balance between efficient development and thorough testing.

Challenges

Before adopting QA.tech, SHOPLAB faced significant testing challenges typical of a scaling company. With frequent releases and a lean internal development team, manually testing their web application was becoming a bottleneck, with bugs slipping through. Alexander Fugah, CEO at SHOPLAB, explains:

“We were releasing new updates almost daily, but it became hard to keep up. Our process was tedious and we needed more coverage without hiring more staff. Ultimately we wanted to rely less on human QA resources.”

With a complex product, SHOPLAB’s most pressing issues included ensuring stability of their many features, while improving the product, testing with different end users data and verifying that they were correctly represented on the platform became critical tasks that were time-consuming to execute manually.

After evaluating their options, SHOPLAB decided to try QA.tech, hoping to scale their testing capabilities and free up internal resources for more critical development tasks.

Solution

SHOPLAB implemented QA.tech to automate their end-to-end (E2E) testing processes, particularly focusing on their most important user flows. Using QA.tech’s dynamic AI testing agent, they set up automated tests for critical areas like managing storefronts, product categories and tax groups. 

One of the key features SHOPLAB found valuable was the ability to integrate QA.tech with their existing CI/CD pipelines using GitHub Actions. This allowed them to run tests automatically after each deployment, ensuring that any errors were caught early in the process. Alexander notes:

“QA.tech fits seamlessly into our workflow. We use GitHub Actions, and it was simple to hook it up to our deployment process. Now, every time we push a release, QA.tech runs the tests. This ensures immediate feedback on potential issues, which has significantly reduced our work load.”

With continuous scanning and feedback, SHOPLAB was able to shift from reactive traditional testing to a proactive automated approach. The AI-driven tests mimicked human behavior, which meant they could handle complex edge cases that a manual tester could have previously missed.

Result

Since integrating QA.tech into their workflow, SHOPLAB has seen a marked improvement in the speed and accuracy of their testing. They are now able to run daily tests across their core features without dedicating human resources to the process.

SHOPLAB’s deployment process has also become more streamlined, with automatic checks in place to ensure that products are correctly imported and displayed. This has resulted in fewer post-release issues and a smoother user experience on their platform. Additionally, the team has noticed a reduction in the number of customer-reported bugs, which they attribute to QA.tech’s thorough testing process.

Conclusion

SHOPLAB turned to QA.tech to solve their growing need for more efficient and comprehensive testing. With daily releases and limited internal resources, they needed a solution that could automate end-to-end testing while catching errors before they reached production.

By using QA.tech, SHOPLAB now benefits from AI-driven automated testing that integrates seamlessly with their CI/CD pipeline. The result is faster, more reliable testing, fewer post-release bugs, and a significant reduction in manual testing time.