Introduction

 To support rapid product innovation across web and mobile applications while maintaining high quality, the engineering team implemented a unified test automation strategy covering all digital touchpoints, including browser, mobile app, API, and AI-driven services.
 This initiative accelerated release cycles, reduced bugs in production, and introduced AI-assisted risk detection, enabling the company to deliver new features faster without compromising quality.

Team size

2 members

Industry

 Software Testing

Technology

Selenium (Test Automation), React Native, Python, AWS, Node.js, Serverless

Highlights

  Value Delivered

  • Accelerated release cycles with parallelized testing across platforms
  • Reduced production bugs through comprehensive automated coverage
  • AI-assisted risk detection for faster issue prioritization
  • Improved test visibility with real-time dashboards and detailed reporting

Challenges

  • Disconnected testing processes for web, mobile, and backend services
  • Manual regression testing slowing down release schedules
  • Lack of AI model validation, increasing potential for unnoticed errors in ML-driven features

Solutions

  1. Unified Automation Framework
  • Consolidated Playwright, Appium, Postman, and AI model validation scripts into a single, modular test framework with shared libraries and consistent reporting. This eliminated the previously disconnected processes for web, mobile, and backend testing.
  1. Automated Regression via Orchestrated CI/CD
  • Integrated GitHub Actions, Jenkins, and AWS CodeBuild to run parallelized cross-platform tests on every code change. Combined with TestRail and Allure Reports, this replaced slow manual regression testing with automated pipelines, cutting average regression time by 40%.
  1. Synthetic Data for AI Model Testing
  • Developed Python-based synthetic data generators to simulate diverse, high-risk scenarios without relying on production data. This ensured robust AI model validation, reducing the chance of unnoticed errors in ML-driven features.
  1. AI-Assisted Risk Detection
  • Implemented ML-powered analytics to detect anomaly patterns in test results and prioritize high-severity issues early in the development cycle, reducing mean time to resolution (MTTR) by 25%.