Knowledge Graph Test Scenario Extraction for Regression Automation
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Solution Overview
Problem
The manual execution of large regression test suites is time-consuming and effort-intensive, and existing automated testing solutions lack efficient management and optimization techniques, leading to suboptimal testing coverage and defect detection.
Innovation Solution
A touchless automated platform system that generates automated testing scripts based on requirements documentation, uses AI and NLP to analyze and sequence test cases, and implements defect solutions, employing data mining, machine learning, and natural language processing to create and maintain enterprise testing suites, optimizing test execution and defect detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual execution of regression test suites is performed, then testing coverage can be achieved, but time consumption and effort increase significantly
Solution Approach 1:
The system enables automated self-execution of test suites through AI-driven test case generation and orchestration. The platform automatically creates test cases from requirements documentation, sequences them optimally, and executes them without manual intervention, allowing the testing process to serve itself rather than requiring continuous human execution.
Solution Approach 2:
The patent replaces manual mechanical execution of tests with automated computational systems. AI algorithms generate test cases, machine learning models optimize test sequencing, and automated frameworks execute test suites, substituting human manual operations with intelligent automated systems that operate faster and more consistently.
2Productivity
If automated testing tools are used, then execution time is reduced, but management and optimization become complex
Solution Approach 1:
The platform provides a universal automated testing system that handles multiple testing functions through a single integrated architecture. It can generate test cases from various requirements formats, support multiple automation tools, optimize test sequencing across different test types, and manage entire test suites, replacing multiple separate tools with one multi-functional platform.
Solution Approach 2:
The system introduces an AI-driven intermediary layer between requirements documentation and test execution. This intermediary automatically translates requirements into structured test cases, optimizes their sequencing using machine learning, and coordinates execution across automation tools, simplifying management by mediating the complex interactions between different testing components.
3Reliability
If more test cases are added to regression suites, then defect detection capability improves, but effort to create and maintain increases
Solution Approach 1:
The system performs preliminary automated generation of test cases from requirements documentation before execution. AI algorithms analyze requirements and pre-generate comprehensive test cases with optimal sequencing, so that when testing begins, all necessary test cases are already prepared and organized, eliminating the need for manual creation effort during test maintenance phases.
Solution Approach 2:
The platform dynamically changes test suite parameters based on learned patterns from execution history. Machine learning models analyze past test results and automatically adjust test case selection, sequencing, and prioritization parameters, enabling the system to maintain high defect detection capability while adapting the test suite composition to minimize maintenance effort based on actual execution outcomes.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving requirements documentation for an application through a user interface (UI), analyzing the requirements documentation to extract terminologies based on an entity term corpus; categorizing the extracted terminologies based on a corpus of known terms; generating a semantic graph from standardized statements constructed from the categorized extracted terminologies; generating a process flow map for the application by identifying processes of the application and a respective relationship between each process from the semantic graph; generating a test scenario map of test scenarios for the application from the process flow map and the semantic graph; and providing the test scenario map to a tester through the UI.