Rendered UI Evaluation Using ML for Visual Error Detection
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Solution Overview
Problem
Existing UI testing methods are inefficient and prone to human error, requiring significant resources and being unable to capture localized features and adapt to scaling invariance, leading to inconsistent and time-consuming manual comparisons.
Innovation Solution
An automated system using machine learning and image transformations to compare UI designs, generating similarity scores and error estimates through optical character recognition, segmentation, and a machine learning architecture, enabling efficient and accurate visual testing across diverse platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual testing is used to compare UI designs, then human testers can identify visual errors, but the process is inefficient and requires significant human resources and time
Solution Approach 1:
The patent replaces the mechanical human visual inspection process with an automated computer-based system using image processing and machine learning algorithms. The system captures UI screenshots, processes them through multiple image transformations (grayscale conversion, histogram equalization, feature extraction), and compares them against reference designs to detect visual errors automatically, eliminating the need for manual pixel-by-pixel comparison while maintaining detection accuracy
Solution Approach 2:
The testing system performs self-evaluation by automatically comparing rendered UI outputs against design specifications without requiring human intervention. The machine learning model autonomously identifies visual discrepancies, generates test results, and provides feedback, enabling the system to serve its own testing needs independently and scale without additional human resources
2Reliability
If multiple testers are deployed to ensure comprehensive UI testing, then coverage improves, but resource consumption and cost increase significantly
Solution Approach 1:
The automated testing system serves multiple testing functions simultaneously - it performs visual regression testing, layout verification, element detection, and cross-device compatibility checking through a single unified platform. The machine learning model can be configured to execute different test scenarios and comparison strategies, replacing the need for multiple specialized testers while maintaining comprehensive coverage
Solution Approach 2:
The testing process is segmented into distinct automated stages: screenshot capture, image preprocessing, feature extraction, comparison analysis, and result generation. Each stage is handled by specialized algorithmic components that work together to provide comprehensive testing coverage, eliminating the need for multiple human testers to perform different aspects of visual inspection
3Adaptability or versatility
If manual resizing of UI images is performed to fit different device screens, then device compatibility testing is enabled, but image quality and pixel accuracy are compromised
Solution Approach 1:
The system handles device compatibility by dynamically adjusting comparison parameters rather than physically resizing images. It transforms UI screenshots to different resolutions and density scales, then applies corresponding transformation parameters to reference designs for fair comparison. This approach maintains original image quality while enabling accurate cross-device compatibility verification through parameter-based adaptation
Data Source
AI summary
Machine learning based computer devices, systems and methods are proposed for automating the evaluation and visual testing of graphical user interface (GUI) designs using a combination of image transformations for scoring the GUI designs and machine learning data architectures with a set of logical and conditional rules. The approach describes an automated process that transforms the GUI designs into clusters of pixels before using a chained series of image transformations to obtain similarity scores and underlying distributions for the GUI designs and then uses a machine learning data architecture in combination with a set of logical and conditional rules to computationally generate a prediction of error estimates based on the underlying distributions of the GUI designs.