UI Error Detection Using Decision Trees and Element Profiles
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Solution Overview
Problem
Existing user interface testing methods are time-consuming, expensive, and prone to human error, lacking the ability to automate the detection of stylistic errors such as those caused by changes in screen size, overlapping elements, and position changes, necessitating manual testing.
Innovation Solution
A method that captures baseline profiles of user interface elements, generates image files, and uses AI-based techniques to detect and store element attributes, allowing automated comparison of pre- and post-update interfaces to identify errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing methods are used to detect user interface errors, then testers can identify interface issues through human review, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces manual mechanical testing processes with an automated computer-based system that captures UI states, extracts element profiles, and performs automatic comparisons. The system substitutes human testers with automated agents that execute test cases and analyze interface changes, thereby reducing testing time while maintaining or improving detection accuracy through consistent, repeatable automated processes.
Solution Approach 2:
The patent creates digital copies of the user interface at different states by capturing screenshots or rendering images of the UI before and after updates. These copied visual representations are then analyzed and compared to detect changes. This copying approach allows for rapid, automated comparison without requiring actual manual inspection of each interface element, significantly reducing testing time while preserving detailed error detection capability.
2Reliability
If comprehensive testing of all interface elements is performed, then all errors can be detected, but the complexity and cost of testing increases
Solution Approach 1:
The patent segments the user interface into individual interface elements by extracting profiles that identify specific elements such as buttons, text fields, and other UI components. Each element is analyzed separately through profile comparison, allowing the system to comprehensively test all elements while managing complexity through modular, element-by-element analysis rather than treating the entire interface as a single complex unit.
Solution Approach 2:
The patent transforms the UI comparison problem into a parameter-based analysis by extracting and comparing specific attributes (parameters) of interface elements such as position, size, text content, and visual characteristics. This parameterization approach simplifies the complexity of comprehensive testing by reducing it to systematic comparison of discrete parameters, making the testing process more manageable and scalable.
3Productivity
If automated testing is implemented, then testing speed increases, but the ability to detect subtle stylistic errors decreases
Solution Approach 1:
The patent replaces manual visual inspection with automated image processing and profile extraction techniques that can detect subtle stylistic changes. The automated system captures visual representations of the UI and analyzes pixel-level or element-level differences, enabling it to detect subtle stylistic errors that might escape human notice while maintaining high testing speed through automated processing.
Solution Approach 2:
The patent creates precise digital copies of the UI interface states and compares them using automated image analysis. By capturing detailed visual information and performing pixel-level or element-level comparison of these copies, the system can detect subtle stylistic changes with high precision while maintaining automated testing speed, overcoming the limitation of traditional automated testing that relies on simpler parameter comparisons.
Data Source
AI summary
An embodiment includes detecting an interface element and an element attribute of the interface element in a series of views of a user interface, and then after an update of the user interface, detecting a candidate element and a candidate element attribute in a series of views of the updated user interface. The embodiment then determines that the updated user interface lacks any errors using a decision tree that includes comparisons of all interface elements of the user interface to corresponding candidate elements of the updated user interface. The embodiment then generates an optimized decision tree based at least in part on an analysis of the comparisons of the user interface to the updated user interface resulting in a condition that allows for the determining of a lack of errors based on comparisons of a subset of the interface elements to corresponding candidate elements.