Test Automation Module for Regression Analytics
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Solution Overview
Problem
Conventional software testing methods require significant manual effort, leading to lengthy test cycles, inadequate test coverage, and reliance on multiple teams, making it challenging to efficiently manage complex software applications with numerous combinations of paths and modules.
Innovation Solution
A data analytics and test automation module that utilizes historical production data to automatically generate fully integrated regression and system testing analytics, reducing the need for multiple tools by aligning fingerprinted tests with a unified test automation framework, facilitating end-to-end UAT, regression, and non-functional testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual test case creation and maintenance is used, then test coverage can be achieved, but the testing time and manual effort increase significantly
Solution Approach 1:
The system enables self-service test case generation by automatically analyzing production data, requirements, and existing test artifacts to create new test cases without manual intervention. The automated system serves itself by continuously generating, executing, and maintaining test cases based on production events and requirements changes.
Solution Approach 2:
The patent replaces the mechanical manual process of test case creation and maintenance with an automated computational system. Instead of manual analysis and script writing, the system uses algorithms to automatically generate test cases from production data, requirements, and existing test artifacts, eliminating the need for manual mechanical effort.
2Reliability
If comprehensive test scenarios are created to cover all paths and modules, then test coverage improves, but the complexity of test script maintenance increases
Solution Approach 1:
The system dynamically adapts test cases to changing production environments and requirements. Test cases are continuously regenerated based on current production data, ensuring they remain relevant and effective without manual updates. The system automatically adjusts test scenarios as the application evolves.
Solution Approach 2:
The patent segments the complex testing process into manageable components: production data collection, requirement analysis, test case generation, execution, and result analysis. Each component handles a specific aspect, reducing overall system complexity while maintaining comprehensive coverage.
3Reliability
If multiple teams are involved to gather test artifacts, then comprehensive testing can be achieved, but the coordination effort and time required increase
Solution Approach 1:
The system serves multiple functions within a single unified platform: collecting production data, analyzing requirements, generating test cases, executing tests, and maintaining test artifacts. This multi-functional approach eliminates the need for multiple separate teams and coordination between them.
Solution Approach 2:
The patent merges previously separate testing activities and team responsibilities into a single automated system. Production data collection, test case creation, execution, and maintenance are combined into one integrated process, eliminating the need for multiple teams to coordinate.
4Reliability
If hand-crafted test cases are used, then specific business scenarios can be covered, but the test cases are not optimized and require continuous manual updates
Solution Approach 1:
The system implements continuous feedback loops where test execution results, production data, and requirements changes automatically trigger test case regeneration. The system learns from production events and continuously optimizes test cases without manual intervention, maintaining up-to-date coverage of business scenarios.
Solution Approach 2:
The system performs preliminary analysis of production data and requirements before test case execution, automatically preparing optimized test cases in advance. This preliminary action ensures test cases are ready and optimized before needed, eliminating the need for last-minute manual updates.
Data Source
AI summary
Various methods, apparatuses/systems, and media for automatically generating fully integrated regression and system testing (FIRST) analytics are disclosed. A processor accesses a production database to obtain production data associated with an application, and accesses a user acceptance testing (UAT) database to obtain UAT data associated with the application. The processor generates gap data on test coverage based on comparing the production data with the UAT data; analyzes the generated gap data; automatically generates, in response to analyzing the generated gap data, executable full coverage of test scenarios for testing the application; and automatically executes testing of the application based on the generated test scenarios.