Our client is a Fortune 500 company and a global player in the FashionTech sector.

The company lacked formal QA processes, regression testing protocols, and test automation frameworks. Only 300 tests were in place, leading to limited test coverage.

ZoolaTech implemented a comprehensive QA strategy from the ground up for both mobile and web platforms. This included manual and automated testing embedded across 7 cross-functional teams, structured as:

• Paired teams: iOS, Android, and Manual QA engineers working together within the same team for:
• High collaboration
• Accurate test coverage
• Deep product understanding
• Maximum delivery efficiency

• Testing backlogs: Created and regularly groomed test backlogs aligned with business objectives to ensure focused and relevant testing
• Scalable architecture: Designed frameworks to minimize maintenance costs and maximize extensibility
• Readable test cases: Ensured clarity and maintainability for easy onboarding and test management
• Network mocking: Implemented mocking techniques to achieve 98% test stability
• CI/CD Integration: Configured pipelines to run thousands of automated tests in under 15 minutes

• iOS: XCUITest (entire Apple ecosystem)
• Android: Espresso (UI testing), Robolectric (unit testing)
• Web: Playwright / Cypress

• iOS:
• StubProxy for network stubbing
• Custom Mutators to extend XCTest functionality
• Android:
• Custom test utilities for improved execution and code coverage

• Gitlab CI/CD: CI/CD setup, optimized for speed and stability
• Test Sharding: Enabled parallel test execution across devices, drastically reducing total runtime

• Allure: Implemented for real-time observability and monitoring of test performance and system behavior

• Continuous improvement: Regular reviews and updates of test cases and strategies to remain aligned with evolving business needs
• Knowledge sharing: Ongoing training and education to build internal QA capabilities and foster talent growth

• Executed ~2,000 tests across iOS and Android
• Achieved 75% test automation coverage
• Reduced manual regression testing time from 48 hours to 8 hours
• Cut full regression suite runtime from 2 hours to 17 minutes using parallel test execution
• Embedded automation for non-functional test cases, including UI, localization, and performance
• Supported continuous QA team growth and skill development for long-term scalability

• Integrated custom-built interceptors into the application layer to automatically detect:
• Missing or malformed event payloads
• Misfired events across key user flows (e.g., sign-up, checkout, onboarding)
• Discrepancies between staging and production environments
• Interceptors flagged one critical issue that could have led to complete revenue attribution loss—preventing misinformed decisions and wasted ad spend
• Embedded analytics validation into the CI/CD pipeline:
• Every release underwent automated tracking checks, just like functional tests
• Resulted in zero tracking gaps at launch
• Accelerated QA cycles and restored trust in analytics accuracy

Impact: Prevented invisible bugs from escalating into costly business failures

• Developed a custom, AI-enhanced visual validation framework to automate localization QA across markets, languages, and devices
• The system successfully:
• Detected overlapping, truncated, or misplaced UI elements
• Flagged untranslated strings and fallback text
• Validated visual layout and semantic accuracy using AI-powered image comparison
• Caught regressions during local content updates in real time
• Prevented a critical UI issue in a high-conversion German user flow, avoiding thousands in lost revenue and reputational damage

Results:

• 95% faster localization QA turnaround
• Near-zero reliance on manual linguist reviews
• Zero critical localization defects in production

Impact: Turned localization QA from a bottleneck into a strategic advantage, ensuring a consistent global user experience