Widget Test Automation via Property Vector Separation
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Solution Overview
Problem
Current approaches to test automation for widgets and structured code modules are inefficient, requiring extensive manual effort and time due to the need for frequent updates and complex user interface features, leading to exponential growth in test cases and maintenance challenges.
Innovation Solution
The implementation of a hyperstructure or hyper-tree approach that separates data acquisition logic from data verification, allowing for flexible, extensible, and scalable testing by generating a widget-specific tree that focuses on relevant features and property vectors for validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual testing approaches are used for widgets, then test coverage can be achieved, but testing efficiency and productivity are low due to extensive manual effort and time requirements
Solution Approach 1:
The system enables self-service testing by automatically generating test cases from widget metadata and executing them without requiring manual test case creation. The framework extracts test information from the widget itself and its documentation, allowing the system to test itself and reducing the need for human intervention in test case generation and execution.
Solution Approach 2:
The patent replaces manual mechanical testing processes with automated computer-based testing mechanisms. The system uses programmatic approaches to generate, execute, and manage test cases automatically, substituting the manual mechanical process of creating and maintaining test cases with an automated software-based system that can perform these tasks independently.
2Adaptability or versatility
If test automation scripts are designed to be reusable and flexible, then adaptability to different testing situations improves, but device complexity and framework design difficulty increase
Solution Approach 1:
The framework segments the testing process into distinct modular components including test case generation module, test execution module, result validation module, and reporting module. Each segment operates independently and can be configured separately, reducing overall framework complexity while maintaining high adaptability. The segmentation allows complex testing scenarios to be broken down into manageable, reusable units.
Solution Approach 2:
The system implements universality by creating a single framework that can handle multiple testing scenarios, widget types, and validation requirements through a unified architecture. The framework uses generic patterns and interfaces that can be applied across different testing situations without requiring separate specialized tools, thereby reducing complexity while increasing versatility.
3Measurement precision
If test cases are generated to cover all user interface features, then measurement precision and test coverage improve, but the quantity of test cases increases exponentially
Solution Approach 1:
The framework extracts only the essential test information needed for validation from the widget metadata and documentation. Instead of generating exhaustive test cases for every possible UI feature, the system extracts and focuses on the critical testable aspects defined in the widget's interface specification, reducing the quantity of test cases while maintaining sufficient coverage for validation purposes.
Solution Approach 2:
The system applies partial action by generating test cases that cover the most important and critical widget features rather than attempting to test every possible scenario. The framework identifies and prioritizes the essential validation requirements based on the widget's functionality, creating a targeted set of test cases that provides adequate coverage without the exponential growth that would result from exhaustive testing.
Data Source
AI summary
Widgets or other code modules for applications written in structured languages such as HTML can be tested advantageously by separating the extraction and validation logic. To test the functionality of a widget in response to an action, widget features of interest are identified. Context and feature nodes for these features are extracted from a page tree structure to generate a property vector that is representative of the state of the widget. After performing the action, context nodes for the features are attached to the new page tree for the newly loaded page, and an observed property vector is generated that represents the state of the widget after the action. The previous property vector is used with updated state information to generate an expected property vector. The observed property vector is compared with the expected property vector, and if the vectors match the validation is claimed to be successful.