Automated Identification Rules for Non-Standard UI Controls
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Solution Overview
Problem
Software and web service applications often face quality assurance challenges due to rushed development, leading to inefficiencies in testing processes, particularly with non-standard controls, which are difficult to identify and support, resulting in bottlenecks and potential errors.
Innovation Solution
A mechanism that includes an accelerator to automatically generate identification rules for non-standard controls, utilizing an agent within the target application to collect properties and generate common identification rules, and a GUI interface for test developers to select and edit these rules, reducing manual effort and expertise requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic testing is performed on web service applications, then testing efficiency is improved, but the complexity of identifying and supporting non-standard controls increases
Solution Approach 1:
The system enables self-service by allowing the automated testing tool to automatically identify and learn non-standard controls through user selections and interactions, eliminating the need for manual configuration of each control type. The tool learns from user behavior patterns to automatically categorize and identify controls.
Solution Approach 2:
A new intermediary component (accelerator/add-in) is introduced between the testing tool and the application controls. This intermediary captures control properties, learns from user interactions, and translates complex control identification into simplified rule-based recognition, reducing the complexity burden on the main testing system.
2Reliability
If rigorous testing is performed before deployment, then application quality is improved, but development time is extended
Solution Approach 1:
The system performs preliminary actions by automatically generating test cases and identifying controls during the development phase rather than requiring separate alpha/beta testing phases. The accelerator learns control patterns early, enabling subsequent automated testing to proceed efficiently without manual intervention.
Solution Approach 2:
Manual testing mechanics are replaced with automated testing mechanics. The system substitutes human testers' manual identification and testing actions with automated rule-based identification and systematic test execution, maintaining quality while reducing time investment.
3Measurement precision
If manual testing is performed, then control identification accuracy is improved, but tester expertise requirements increase
Solution Approach 1:
The system performs self-learning by automatically capturing control properties and learning from user interactions. The accelerator monitors how users select and interact with controls, automatically generating identification rules without requiring testers to manually document or analyze each control type.
Solution Approach 2:
The accelerator acts as an intermediary that bridges the gap between simple user actions and complex control identification. It captures subtle user behaviors and translates them into accurate identification rules, maintaining high precision while requiring minimal user expertise.
4Speed
If automated testing tools are used, then testing speed is improved, but adaptability to custom controls deteriorates
Solution Approach 1:
The system is made dynamic through continuous learning from user interactions. The accelerator adapts its control identification rules based on observed user behavior patterns, allowing the automated tool to dynamically adjust to custom and non-standard controls while maintaining testing speed.
Solution Approach 2:
The testing system performs self-adaptation by automatically learning control patterns from user interactions. Rather than requiring manual configuration for each custom control type, the system autonomously learns and adapts to new control types, maintaining both speed and adaptability.
Data Source
AI summary
A mechanism is disclosed for identifying non-standard user interface controls in a target application. The mechanism includes an accelerator, an agent, and a dialog. The agent is configured to be installed into the target application and to interface with the accelerator. The dialog is configured to select non-standard controls in the target application where each non-standard control includes a set of properties. The agent is configured to provide the properties of the selected non-standard controls to the accelerator. The accelerator is configured to determine a set of common properties for the selected non-standard controls and to automatically generate an identification rule for the determined set of common properties.