AI Test Scenario Generation with Adaptive Model Feedback
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Solution Overview
Problem
Manual test scenario generation in software testing is inefficient, leading to inadequate test coverage, inconsistency, and difficulty in adapting to changing requirements, particularly in large and complex projects, resulting in decreased software quality and increased costs.
Innovation Solution
A system that uses a comprehensive software behavior model integrated with artificial intelligence to automatically generate test scenarios, incorporating user habits and system interactions, and dynamically updates based on test results to ensure comprehensive and adaptive testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual test scenario generation is used, then test engineers can create scenarios based on their experience, but test coverage is inadequate and critical errors may be overlooked
Solution Approach 1:
The patent replaces the manual mechanical process of test scenario creation with an automated AI-based system. The AI agent analyzes requirement documents, user stories, and system models to automatically generate comprehensive test scenarios, eliminating reliance on human engineers' subjective experience while improving both coverage and efficiency
Solution Approach 2:
The system enables self-service test scenario generation where the AI autonomously creates, executes, and evaluates test scenarios without continuous human intervention. The system learns from execution results and automatically refines its scenario generation capabilities, making the testing process self-improving
2Adaptability or versatility
If manual test scenario generation is used, then scenarios can be created based on current knowledge, but updating scenarios when requirements change is time-consuming and error-prone
Solution Approach 1:
The system implements continuous feedback loops where test execution results are automatically analyzed and fed back to the AI agent. This feedback mechanism enables the system to detect requirement changes and automatically update test scenarios, ensuring adaptability without manual intervention
Solution Approach 2:
The test scenario generation system is designed to be dynamic and adaptive rather than static. The AI continuously monitors requirement documents and system models, automatically adjusting test scenarios to reflect current system state and requirements, making the system inherently adaptable to changes
3Stability of the object's composition
If manual test scenario generation is used, then test engineers have control over scenario creation, but inconsistencies occur between different engineers testing the same requirements
Solution Approach 1:
The AI-based system serves as a universal test scenario generator that processes all requirements through the same analytical framework. It multi-functionally handles requirement analysis, test scenario generation, execution, and evaluation, ensuring consistent application of testing logic across all scenarios regardless of which engineer initiated the process
Data Source
AI summary
In software testing processes, a system that automatically creates test scenarios using a comprehensive model representing the general system behavior, along with resources such as requirement documents and user stories, and constantly updates the model by analyzing past test results. A method is also disclosed.