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Industry: Sports
Location: United Kingdom
Scope: Web/Mobile
Client Overview:
Our client, a leading golf-technology innovator, aimed to encourage more people to play golf by providing technology-enabled solutions. The platform consolidates individual on- and off-course data sources into a single login, harmonies the data, and delivers comprehensive insights to players and coaches. Their vision was to create a connected golfing experience where accurate analytics enhance performance and user engagement.
Goals
- Build user confidence by ensuring accurate and relevant data for analysis.
- Optimize QA to support frequent deployments with minimal risk.
- Reduce reliance on manual testing through a scalable automation framework.
- Validate data accuracy and workflows (upload, edit, update, delete).
- Guarantee seamless functionality across web and mobile platforms.
- Ensure the platform is responsive and scalable to support a growing user base.
Challenges:
- Maintaining accuracy of golf performance analytics across multiple data streams.
- Introducing new features without compromising existing workflows.
- Testing complex data integrations and analytics dashboards.
- Managing extensive regression cycles across devices and browsers.
- Identifying UI regressions in charts and data-heavy dashboards.
Tools & Technologies:
- TypeScript & VS Code — Development and scripting environment.
- Cypress — Core automation framework for web UI and workflows.
- Appium — Mobile automation framework for iOS and Android apps.
- AWS CodeCommit & LambdaTest — Version control, CI/CD, and scalable cross-browser testing.
- GitHub Actions + Gemini API — Build automation with intelligent test analysis and reporting.
- JMeter — Load and performance testing up to 1,000 concurrent users.
- Visual Testing Tools — Automated screenshot capture and comparison.
- Git Bash — CLI for streamlined workflows.
- Slack — Real-time test reporting and collaboration.
Solution:
Phase 1: Establishing a Robust Automation Framework
- Designed and implemented a scalable automation framework using Cypress with the Page Object Model (POM).
- Eliminated hardcoding by introducing parameterization and reusable utilities.
- Migrated and refactored existing scripts to align with the new structure.
- Converted all critical manual test cases into automated scripts, covering end-to-end scenarios.
- Organized test cases into smoke and regression suites for streamlined execution and stakeholder review.
Phase 2: Cross-Browser Testing and Performance Validation
- Integrated the framework with LambdaTest for parallel cross-browser execution across multiple environments, including headless browsers.
- Leveraged LambdaTest dashboards for test execution monitoring and reporting.
- Conducted performance testing using JMeter, simulating up to 1,000 users to evaluate scalability and responsiveness.
- Identified and addressed performance bottlenecks before production releases.
Phase 3: Visual Testing for UI Accuracy
- Introduced visual regression testing, capturing and comparing screenshots to quickly detect UI discrepancies.
- Focused on validating charts, graphs, and leaderboards, ensuring accuracy in visual presentation of golf performance analytics.
- Reduced reliance on manual UI checks and accelerated release validation.
Phase 4: Extending Automation to Mobile Platforms
- Expanded the automation framework to support both desktop and mobile applications.
- Developed mobile automation scripts using Appium to validate core flows such as login, data synchronization, and analytics dashboards.
- Ensured cross-platform parity, delivering a consistent experience for golfers and coaches on all devices.
Phase 5: Dual Build Pipelines with AWS & Gemini
- Established two complementary build structures for flexibility and resilience.
- AWS + LambdaTest: Managed scalable cross-browser regression and parallel execution using AWS infrastructure integrated with LambdaTest.
- GitHub Actions + Gemini API: Leveraged Gemini API for intelligent automation build analysis, providing in-depth visibility into build health and test stability within GitHub Actions workflows.
- Enabled faster root-cause detection for flaky tests and improved build-overview reporting.
- Delivered a more reliable and transparent CI/CD ecosystem by balancing execution scalability with intelligent insights.
Key Benefits & Outcomes:
- Accurate visual verification: Automated visual testing for data-heavy dashboards reduced UI-related defects.
- Faster release cycles: Smoke and regression suite separation, combined with parallel execution, shortened feedback loops.
- Improved scalability: Load testing validated the platform’s ability to handle thousands of users concurrently.
- Enhanced collaboration: LambdaTest dashboards, Gemini analysis, and Slack integration provided transparency for QA, Dev, and Product teams.
- Higher automation coverage: Automated validation of add/edit/delete/update flows reduced manual effort and errors.
- Early defect detection: Regression issues were identified earlier, lowering production bug rates.
- Future-ready framework: Flexible design allowed easy expansion to mobile automation and dual build pipelines, ensuring long-term QA scalability.
Testrig’s end-to-end QA services empowered the client to deliver a connected golfing experience backed by reliable analytics, scalable performance, and a consistent multi-platform user journey.
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