Comparable UI Object Identification via Data Correlation
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Solution Overview
Problem
Existing UI testing methods face challenges in identifying unique and stable UI objects due to shared attributes, leading to unstable automation scripts when attributes change during testing.
Innovation Solution
A system captures and correlates data points during test executions to identify comparable UI objects based on interaction context and attribute similarity, providing a visualization to determine unique and stable attributes for script generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If UI objects are identified based on common attributes, then identification is simplified, but uniqueness and stability of identification deteriorate
Solution Approach 1:
The patent segments UI object identification into multiple independent attribute dimensions (location, size, text content, hierarchical path, etc.) rather than relying on a single common attribute. This segmentation allows the system to evaluate multiple attributes simultaneously, maintaining simplicity while improving uniqueness and stability through multi-factor identification.
Solution Approach 2:
The patent creates a composite identification signature by combining multiple UI object attributes (location coordinates, dimensions, text content, parent-child relationships, etc.) into a unified identification framework. This composite approach ensures that even if one attribute changes or is shared, the combination maintains unique and stable identification across AUT instances and versions.
2Ease of manufacture
If automation scripts use attribute-based UI object identification, then script generation is easier, but script stability deteriorates when attributes change
Solution Approach 1:
The patent performs preliminary analysis of UI object attributes across multiple AUT instances and versions before generating automation scripts. By pre-evaluating attribute uniqueness, stability, and comparability, the system identifies the most reliable attributes for script generation in advance, ensuring scripts remain stable even when UI attributes change during testing or updates.
Solution Approach 2:
The patent implements a feedback mechanism that continuously monitors UI object attribute changes across AUT instances and versions. This feedback informs the script generation process by highlighting stable, unique attributes that should be used for identification, allowing the system to adapt to UI changes while maintaining script stability through data-driven attribute selection.
Data Source
AI summary
Example implementations relate to comparable UI object identifications. Some implementations may include a data capture engine to capture data points during test executions of the application under test. The data points may include, for example, test action data and application action data. Additionally, some implementations may include a data correlation engine to correlate each of the data points with a particular test execution of the test executions, and each of the data points may be correlated based on a sequence of events that occurred during the particular test execution. Furthermore, some implementations may also automatically identify, based on the correlated data points, a set of comparable UI objects.