Screencast-Based Analysis of User-Perceived GUI Responsiveness

TL;DR

This paper introduces ool, a novel black-box method that uses computer vision to analyze mobile screencasts for user-perceived GUI responsiveness issues, improving detection accuracy over existing techniques.

Contribution

The paper presents ool, a lightweight, scalable screencast analysis tool that directly measures user-perceived responsiveness metrics using computer vision, applicable in industrial testing pipelines.

Findings

High accuracy in interaction detection (0.96 precision, 0.93 recall)

Responsive time measurement within 50-100 ms error for most interactions

Uncovered responsiveness issues missed by traditional static analysis methods

Abstract

GUI responsiveness is critical for a positive user experience in mobile applications. Even brief delays in visual feedback can frustrate users and lead to negative reviews. However, detecting and quantifying such user-perceived delays remains challenging, especially in industrial testing pipelines that evaluate thousands of apps daily across diverse devices and OS versions. Existing techniques based on static analysis or system metrics, while useful, may not accurately capture user-perceived issues or scale effectively. In this experience paper, we present \tool, a lightweight and black-box technique that measures GUI responsiveness directly from mobile screencasts -- video recordings captured during automated GUI testing. \tool detects user interactions and visual delays, helping developers identify GUI performance issues that affect the user experience. It uses computer vision to detect user interactions and analyzes frame-level visual changes to compute two key metrics: response time (from user action to first visual feedback) and finish time (until visual feedback stabilizes). We evaluate \tool on a manually annotated benchmark of 2,458 interactions from 64 popular Android apps. \tool achieves 0.96 precision and 0.93 recall in detecting interactions, and measures response and finish times within 50\,ms and 100\,ms error, respectively, for over 89\% of interactions. The tool has been deployed in an industrial testing pipeline and analyzes thousands of screencasts daily, uncovering responsiveness issues missed by traditional tools and improving performance debugging efficiency.