Enterprise QA Automation: How We Cut Regression Time by 40%

When you operate a large-scale SaaS platform that powers customer experience (CX), social media management, and digital advertising for the world’s biggest brands, “downtime” is not an option. For our client, a global enterprise platform, managing quality across such a vast ecosystem was becoming a bottleneck.

The solution lay in transforming their manual testing processes into a sophisticated enterprise QA automation engine. This case study details how Qualitrix, operating through a Global Capability Center (GCC) model, helped this client scale their quality engineering, reduce regression cycles, and improve release reliability.

The Challenge: Complexity at Scale

The client’s platform is a behemoth, enabling enterprises to manage customer feedback, contact center operations, and social engagement across web and mobile. With such a wide array of functionalities, their QA challenges were significant.

• Vast Functional Scope: The testing scope included Social Media Management, Customer Experience (CX) Case Management, CRM Integrations, and high-stakes Digital Advertising platforms.

   

• The Regression Bottleneck: With 1,500 to 2,000+ test cases needing execution per cycle, manual regression was too slow to keep up with their agile release cadence.

   

• Diverse Tech Stack: Effective enterprise QA automation had to cover everything from web applications and mobile apps (iOS & Android) to complex API integrations.

   

Our Solution: A Multi-Layer Enterprise QA Automation Strategy

To tackle this scale, Qualitrix implemented a robust automation strategy that targeted every layer of the application stack. We didn’t just automate scripts; we built a self-sustaining quality ecosystem.

1. Full-Stack Automation Framework

We utilized a “best tool for the job” approach to ensure maximum stability and coverage:

• Web Automation: We leveraged Selenium and Selenide (Java) to create robust scripts for the core web platform.

• Mobile Automation: For the mobile apps, we utilized Appium to handle both Android and iOS environments efficiently.

• API Level Testing: We bypassed the UI where possible to test logic faster using RestAssured for API automation.

2. CI/CD Integration for Continuous Quality

Enterprise QA automation provides the most value when it is continuous. We integrated our suites directly into the client’s Jenkins pipelines. This allowed for automated smoke tests and nightly regression runs, providing developers with immediate feedback every morning.

3. Parallel Execution for Speed

To further reduce cycle time, we implemented parallel execution. This allowed us to run hundreds of tests simultaneously, turning what would be a days-long process into a matter of hours.

The Business Impact

The transition to an automated GCC model delivered transformative results. By shifting the heavy lifting to our enterprise QA automation framework, the client was able to accelerate their development velocity without sacrificing quality.

• 40% Faster Regression: The most immediate win was a ~40% reduction in regression execution time. This massive time saving allowed the client to release updates more frequently.

• 65% Automation Coverage: We achieved 60–65% automation coverage across the platform, ensuring that the majority of critical flows were validated without human intervention.

• Revenue Stability: By automating the testing of revenue-critical ad workflows, we improved the stability of their monetization features, protecting their core business income.

• Reduced Defect Leakage: The consistent, repeatable nature of the automated suites significantly reduced the number of defects leaking into production.