Automated Browser Testing via Dynamic Value Extraction
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Solution Overview
Problem
Current automated testing tools for browser-based applications require manual updates and are unable to dynamically recognize and test user interface controls across different browser software, device operating systems, and hardware platforms, leading to inefficiencies and the need for multiple test scripts for various platforms.
Innovation Solution
A system that generates a baseline test script with empty user interface component values, which is executed in a learning mode to extract expected values for specific platforms and layouts, and then in a testing mode to verify actual values, allowing for seamless adaptation and reuse across different platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate test scripts are created for each platform, then testing coverage across different platforms is improved, but device complexity and maintenance burden increase
Solution Approach 1:
The patent implements a universal test script that can execute across multiple device platforms (desktop, mobile, tablet) and browser types. The system uses a single baseline test script that dynamically adapts to different platforms through automated parameter extraction, eliminating the need for separate test scripts for each platform while maintaining comprehensive testing coverage.
Solution Approach 2:
The system changes parameters dynamically during test execution based on the target platform. It extracts platform-specific parameters (such as device type, browser version, screen resolution) and automatically adjusts test parameters accordingly, allowing one test script to serve multiple platforms with varying configurations.
2Manufacturing precision
If manual updates are performed for each platform, then test accuracy is improved, but productivity and efficiency deteriorate
Solution Approach 1:
The system performs self-service by automatically extracting expected parameter values from reference implementations during a learning phase. It stores these extracted values and automatically retrieves them during testing, eliminating the need for manual updates while maintaining high test accuracy across all platforms.
Solution Approach 2:
The system performs preliminary action by conducting a learning phase before actual testing, where it extracts and stores expected parameter values for each platform. This pre-processing allows the system to automatically fill in platform-specific parameters during testing without manual intervention, improving both accuracy and efficiency.
3Measurement precision
If test scripts are made platform-specific, then measurement precision is improved, but adaptability and reusability worsen
Solution Approach 1:
The system implements dynamics by making the test script adaptable rather than static. It dynamically extracts platform-specific parameters during execution and automatically adjusts test configurations based on the target platform, allowing the same script to maintain measurement precision across diverse platforms without being locked into platform-specific implementations.
Data Source
AI summary
Methods and apparatuses are described for intelligent automation of computer software test scripts and code requirements. A server generates a baseline test script for a plurality of browser-based applications with a sequence of action steps having one or more empty user interface component values. The server performs a first execution of the baseline test script against each of the browser-based applications to extract one or more expected values associated with user interface components that correspond to the empty user interface component values, and stores the expected values. The server performs a second execution of the baseline test script against the browser-based applications to extract one or more actual values associated with user interface components that correspond to the empty user interface component values. The server compares the actual values against the expected values, and if the values do not match, the server generates a message indicating a test failure.