Cross-Browser UI Testing via Structural Similarity Index
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Solution Overview
Problem
Current test automation processes for web applications are inefficient in accurately testing across different platforms, relying on manual comparison and placing a significant burden on technical support staff, leading to suboptimal user experiences due to undetected anomalies.
Innovation Solution
A computerized method and system for automated cross-browser user interface testing using image processing and optical character recognition to compare UI images across platforms, employing structural similarity index measures and highlighting divergent regions, enabling efficient detection and notification of errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual comparison methods are used to test web applications across different platforms, then the testing process can be performed with simple tools, but the testing efficiency is low and the burden on technical support staff is high
Solution Approach 1:
The patent replaces manual mechanical comparison processes with automated image processing and optical character recognition systems. The system automatically captures screenshots from multiple platforms, processes images to normalize differences, compares UI elements using algorithms, and generates anomaly reports without human intervention, thereby dramatically improving testing efficiency while managing complexity through automation.
Solution Approach 2:
The testing system performs self-service by automatically executing test cases, capturing interface images, processing and comparing the images, identifying anomalies, and generating reports. The system serves itself by maintaining test databases, updating baseline images, and continuously improving its comparison algorithms without requiring external manual operation for each test cycle.
2Productivity
If automated testing systems are implemented to improve testing efficiency, then productivity increases, but the device complexity and implementation cost increase
Solution Approach 1:
The patent creates a universal testing system that can handle multiple browser types, operating systems, and device platforms through a single automated framework. The system uses platform-agnostic image capture and processing techniques that work across different environments, allowing one system to perform multiple testing functions rather than requiring separate tools for each platform combination.
Solution Approach 2:
The system introduces image processing as an intermediary layer between raw screenshot capture and UI comparison. This intermediary processing step normalizes images by removing platform-specific rendering differences, converts images to comparable formats, and prepares them for algorithmic analysis, thereby simplifying the overall system architecture while enabling cross-platform testing.
3Measurement precision
If advanced image processing techniques are used to accurately detect UI anomalies, then measurement precision improves, but the computational resources and time required increase
Solution Approach 1:
The patent segments the image comparison process into distinct phases: initial rapid screening using structural similarity algorithms to identify potentially different regions, followed by detailed analysis only of those specific regions using optical character recognition and element-level comparison. This segmentation allows the system to maintain high precision while reducing overall processing time by avoiding exhaustive full-image analysis.
Solution Approach 2:
The system applies partial action by focusing computational resources only on regions of interest identified during preliminary analysis. Rather than uniformly processing entire images at high resolution, the system performs detailed analysis only on areas where anomalies are detected, using lower-resolution processing for confirmed matching regions, thereby optimizing the balance between precision and processing time.
Data Source
AI summary
Methods and apparatuses are described for automated cross-browser user interface testing. A computing device captures (i) a first image file corresponding to a first current user interface view of a web application on a first testing platform and (ii) a second image file corresponding to a second current user interface view of a web application on a second testing platform. The computing device prepares the image files, and compares the prepared image files using a structural similarity index measure. The computing device determines that the prepared first image file and the prepared second image file represent a common user interface view when the structural similarity index measure is within a predetermined range. The computing device highlights corresponding regions that visually diverge from each other in each of the prepared image files and transmits a notification message comprising the highlighted image files.