UI Anomaly Detection Engine for Text Truncation
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Solution Overview
Problem
Existing methods for detecting user interface (UI) anomalies in software applications, particularly during globalization, are inefficient and prone to human error, as they rely on manual inspection across various languages and devices, leading to potential UI issues like truncation and overlap that can degrade user experience.
Innovation Solution
A detection engine is injected into the client application as a worker thread to monitor UI elements and detect anomalies such as text truncation and overlap by comparing the size of UI elements with their content, providing feedback to a server for automated issue identification and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection is used to detect UI anomalies, then human judgment can identify issues, but the process is inefficient and prone to human error
Solution Approach 1:
The patent replaces manual visual inspection with an automated detection engine that uses computational methods to analyze UI elements. The system captures screenshots, processes images to detect truncation and overlap, and automatically generates anomaly reports, eliminating human error and significantly improving inspection efficiency while maintaining high detection accuracy
Solution Approach 2:
The detection engine operates autonomously within the application process, automatically monitoring UI elements without requiring external intervention. The system self-manages the entire inspection workflow from capturing UI states to generating anomaly reports, enabling continuous automated testing without manual involvement
2Adaptability or versatility
If manual inspection is performed across various languages and devices, then comprehensive coverage is achieved, but the process is time-consuming and slow
Solution Approach 1:
The automated detection engine processes UI elements across multiple languages and devices simultaneously, capturing and analyzing screenshots from different language versions and device types in parallel. This automated approach maintains comprehensive multi-language coverage while reducing inspection time from days to minutes
Solution Approach 2:
The system performs preliminary detection of UI anomalies before product deployment by integrating the detection engine into the build process. UI elements are automatically inspected for truncation and overlap issues in all supported languages and devices prior to release, enabling early identification and resolution of problems
3Reliability
If the detection engine monitors all UI elements continuously, then comprehensive anomaly detection is achieved, but the computational overhead increases
Solution Approach 1:
The detection engine focuses computational resources on specific UI elements that are prone to anomalies, such as text fields and buttons that may experience truncation or overlap. Rather than uniformly analyzing all UI elements, the system applies targeted detection to high-risk areas, maintaining detection completeness while reducing overall processing overhead
Data Source
AI summary
Embodiments described include systems and methods for user interface (UI) anomaly detection. One or more processors of a client device can execute an application undergoing UI anomaly detection. The application can be injected with a detection engine. The detection engine can determine, while executing as a thread of the application on the one or more processors of the client device, that a dimension of a text-designated region of a first user interface element of the application is less than that of corresponding text for rendering on the user interface element. The detection engine can provide, to a server responsive to the determination, an indication of a first UI anomaly. The indication can include information about a position and size of the first user interface element.