Phantom Rendering Detection: Identifying and Analyzing Unnecessary UI Computations

Modern mobile applications have high-resolution user interfaces (UI) and heavy computations, resulting in significant energy consumption and latency, especially on older devices. Precisely measuring the performance of mobile operations (e.g., the number of CPU instructions) and detecting performance issues are critical for mobile software engineering. In this study, we characterize a previously underexplored class of performance issues on mobile called Phantom Rendering, which occurs when mobile applications perform unnecessary UI-related offscreen computations but do not visually render them. For example, the animation component stops visually rendering on the screen but continues to refresh in the background. This problem represents a root-level disconnection between UI-related offscreen computational effort and visual rendering, inherent to dual-thread rendering architectures employed across modern mobile platforms such as Android, iOS, and OpenHarmony. While this architectural pattern is shared across platforms, our current implementation and evaluation focus on OpenHarmony. However, this is hard to detect automatically due to a lack of fine-grained performance measurements and detection methods. To address the challenges, we propose HapPRDetection that contains a fine-grained performance profiler that can sample CPU Retired Instructions, the CPU instructions that have completed their execution and are no longer in the pipeline, and algorithms for automated detection of Phantom Rendering through differential analysis and hierarchical attribution. Our approach advances performance analysis methodology by bridging the semantic gap between fine-grained computational measurements (i.e., the number of CPU Retired Instructions at the function level) and high-level rendering behavior. Through our evaluation of the top-22 real-world mobile applications by download volume in OpenHarmony with 193 test steps, we show that Phantom Rendering issues occur in 19 test cases across 8 applications and that they range up to 40% of wasted CPU instructions in each problematic operation. We provide new insights into mobile rendering efficiency and our detection strategy offers practical solutions to identify and resolve Phantom Rendering during the mobile development loop. Our approach and implementation are available at https://github.com/SMAT-Lab/PhantomRendering.git.