Self-Healing UI Testing Using Machine Learning Flow Detection
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Solution Overview
Problem
Current automation testing tools are cumbersome, costly, and inaccessible to users without specialized programming knowledge, requiring significant time and resources for script modifications and maintenance, especially when integrating with user interfaces and multiple frameworks.
Innovation Solution
An automated user interface testing system using a deep learning training model that integrates self-healing capabilities, allowing users to input a URL and leveraging a recurrent neural network to detect and resolve UI changes autonomously, reducing the need for specialized programming skills and enhancing accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If current automation testing tools are used, then testing can be automated, but the tools are cumbersome and require specialized programming knowledge
Solution Approach 1:
The patent introduces an AI-based intermediary layer that mediates between the user and the complex automation testing framework. This intermediary automatically generates, updates, and maintains test scripts by observing UI changes, eliminating the need for users to directly interact with complex programming frameworks while preserving automation capabilities
Solution Approach 2:
The testing system performs self-service by automatically detecting UI changes, generating appropriate test script updates, and maintaining itself without human intervention. The system observes UI element changes and autonomously updates the automation scripts to adapt to these changes, freeing users from manual script maintenance
2Reliability
If automation scripts are manually created and maintained, then testing coverage can be achieved, but significant time and resources are required
Solution Approach 1:
The system implements continuous feedback loops where UI changes are automatically detected and fed back to the script generation engine. This feedback mechanism enables the system to automatically update test scripts in response to UI modifications, maintaining comprehensive testing coverage without manual intervention and significantly reducing time loss
Solution Approach 2:
The system performs preliminary actions by proactively detecting UI changes before they break existing test scripts. By continuously monitoring the UI and preparing script updates in advance, the system prevents testing gaps and maintains coverage without requiring reactive manual script maintenance
3Adaptability or versatility
If multiple automation frameworks are implemented for different databases, then comprehensive testing across databases is achieved, but device complexity and cost increase
Solution Approach 1:
The patent implements a universal AI-based testing framework that can adapt to multiple database types and UI frameworks through a single unified system. The AI engine automatically generates database-specific test scripts based on the target database characteristics, eliminating the need for separate specialized frameworks while maintaining comprehensive multi-database testing capability
Solution Approach 2:
The system manages complexity by dynamically changing parameters such as database connection strings, UI element locators, and test data based on the target system being tested. This parameter-driven approach allows a single framework to adapt to multiple databases without requiring separate framework implementations for each database type
Data Source
AI summary
Systems and methods are provided for implementing automated user interface testing with integrated machine learning models. Systems and methods for detecting and preemptively correcting flow path errors are disclosed. Systems and methods for minimizing user input and optimizing testing efficiency are disclosed. A result dashboard is disclosed in which testing results and errors are displayed and a user may interact with interactive testing reports.