AI-Based Compatibility for Android Fragmentation: Streamlining Global Streaming Apps
Ani Modi

AI-Based Compatibility for Android Fragmentation: Streamlining Global Streaming Apps

Challenge

The fragmentation of Android posed significant challenges, as there were more than 24,000 different smartphone models with various hardware, screen dimensions, and OS versions (Android 8.0 to 14).

  • Device incompatibility: Apps crash often on low-resource devices.
  • User interface disparities: Consistency of uneven layouts at non-standard resolutions.
  • Testing Overload: Slow and limited manual device testing.
  • Performance Gaps: Users were less happy when apps ran unevenly.

These issues undermined revenue and market share through a 15% user attrition rate and delayed new releases.

Solution

To address the primary issues caused by Android fragmentation, Lucent Innovation developed a systematic, AI-based solution that provides performance and compatibility for 95% of users while making testing easier.

  1. Addressing Device Incompatibility

To solve the issue of recurrent app crashes on low-end devices, we initially applied a machine learning model using TensorFlow to classify and cluster devices based on user behavior patterns, OS versions (Android 8.0 through 14), and hardware configurations (e.g., CPU and RAM). Through the identification of representative devices that captured 95% of the user base, this clustering ensured compatibility and reduced the scope of testing.

  1. Resolving Inconsistencies in User Interfaces

Inconsistent layouts on non-standard resolutions were the second issue. To provide a uniform user interface across various device profiles, we utilized Jetpack Compose to design responsive, adaptive layouts that automatically adjust according to different screen resolutions and sizes.

  1. Reaching Beyond the Test Overload

The next challenge was that manual device testing was a slow and limited process. To rapidly test UI rendering and behavior across many combinations of hardware and OS, we employed AI-driven Robolectric to run thousands of virtual device configurations in simulation.

  1. Minimizing Performance Deficits

Unbalanced app performance was the second issue, particularly on low-end devices. We utilized PyTorch models to predict resource demands (CPU and memory) per device category and included Android App Bundles to reduce APK files by 30%, optimizing low-resource device performance.

  1. Ensuring Certain Real-Device Reliability

Verification of real devices was the next step. Employing Espresso and cloud device farms, we built an automated test bed to validate app performance in real usage and ensure reliability across a portfolio of devices.

  1. Reducing Development Cycles

Gradual testing processes delayed feature releases, which was the second issue. We implemented CI/CD pipelines with Jenkins and GitHub Actions, which enabled rapid, reliable feature deployment and ongoing testing on device clusters. These tools are essential for teams that frequently hire Android developers and aim for continuous delivery with minimal risk.

  1. Enhancing Performance Monitoring

Finally, we have incorporated Firebase Analytics and telemetry measures to monitor user behavior, crash data, and app performance in real time, meeting the demands for constant enhancements. Stability and user experience were enhanced through iterative enhancements instigated through feedback cycled into the AI model.