The Opportunity
With a staggering 66 million memberships, gyms are experiencing an all-time high. However, the reality is starkly different. A significant 72% of gym-goers are now opting for home workouts at least once a week, a direct reflection of the pervasive work-from-home trend. This shift in consumer behavior has forced gyms to reevaluate their strategies and adapt to the evolving fitness landscape.
To remain relevant, the customer at the center of this case study caters to the needs of a hybrid fitness consumer, offering innovative solutions that bridge the gap between traditional gym experiences and the convenience of home workouts.
Its existing platform provided fully interactive instructor-led workouts, including optional end-user video and audio sharing capabilities. However, managing and maintaining the various platforms was largely manual and time-consuming, with no established testing process or automation solutions.
Key Challenges
The main challenges the customer wanted to address were as follows:
- Separate automation frameworks for web, mobile, and TV were difficult to manage. As a result, separate reports were generated for each platform which did not provide an overall picture of the health of the application.
- Testing the applications on multiple mobile devices/TV and OS versions was becoming more and more challenging to maintain.
- There was no existing regression suite available to automate the application.
- The difficulty in validating the video streams due to content changes was further complicating the validation process.
The Solution
In order to address the challenges above, the customer engaged SourceFuse as a result of being strongly recommended by AWS. This was based on several factors, including being an AWS Premier Tier Services partner, which validates SourceFuse’s commitment to providing best-in-class cloud-native solutions on AWS. In addition, SourceFuse’s AWS competencies align with the customer’s unique needs, supporting it to realize its business goals. The client was looking to modernize its current application, for which ARC by SourceFuse also made a compelling proposition to move ahead with this project.
The solution SourceFuse proposed included the following:
- Tools Used: Selenium, Appium
- Language: Java
- Automation Framework: Designed and implemented a customized hybrid test automation framework incorporating the Page Object Model (POM) to meet multi-platform requirements, including Web, Mobile, and TV. Built using Maven, the framework features:
- Seamless integration with Allure Reporting for detailed test analysis.
- A customizable logging mechanism for enhanced debugging.
- A dynamic locator strategy to handle complex UI interactions.
- Parallel execution capabilities using TestNG and Selenium Grid to optimize testing time.
- Custom utilities for visual validation and image comparison for cross-platform consistency.
- Test data management through integration with APIs and external sources like Excel, JSON, and databases and additional modular components to ensure scalability and reusability.
- Leveraging the use of AWS Device Farm to test with multiple devices and OS versions.
- Collaboration with the team to build the regression suite, of which would ensure coverage for all platforms.
- Development of a robust solution for validating video streaming by implementing mobile gestures and capturing screenshots at regular intervals. Our solution leveraged the ImageDiffer library in conjunction with the Screenshot class from Selenium to efficiently perform pixel-by-pixel comparison and verify the visual consistency of video streams.
Application Features:
- Workout Plans
- Personalized Training
- Workout Scheduling
- Community and Support
- Virtual Coaching
- Social Features
The Results
SourceFuse’s solution provided the customer with a unified framework, helping them to achieve automated video streaming tests to expand coverage and minimize manual processes. Leveraging AWS Device Farm enabled automation support, and in addition hardware costs were significantly reduced. The new fully automated regression suite improved quality and reduced defect slippage and manual regression efforts.
- 30% reduction of Git management effort
- 50% reduction in report analysis time
- 91% automation regression coverage
- 80% reduction in manual regression effort
- 0% defect leakage during testing
About The Customer
Founded in 2015 and headquartered in the US, this customer provides the ultimate fitness streaming platform. Offering both white-label solutions for businesses and direct-to-consumer access to live and on-demand classes from top global studios and trainers. Today, it serves over 550k members in 39 countries specializing in wearable technology, boutique fitness studios, and workout videos.
