AI Test Scenario Generation from Software Behavior Models
Find Innovative SolutionsGenerate Solutions
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, especially in large and complex projects, resulting in decreased software quality and increased costs.
Innovation Solution
A system that uses a comprehensive software behavior model and artificial intelligence to automatically generate and update test scenarios based on user habits, system interactions, and operating flows, integrating multi-source data for improved coverage and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual test scenario generation is used, then test scenarios can be created with human expertise, but test coverage is inadequate and time-consuming
Solution Approach 1:
The system enables self-service test scenario generation by automatically creating test scenarios from user stories and system models without requiring manual intervention from test engineers. The AI algorithms autonomously analyze requirements and generate comprehensive test scenarios, eliminating the time-consuming manual process while maintaining high test coverage through systematic analysis of all possible test cases.
Solution Approach 2:
The patent replaces the mechanical manual process of test scenario creation with an automated AI-based system. Instead of test engineers manually writing scenarios based on their expertise, the system uses machine learning algorithms to automatically generate test scenarios from user stories and system models, significantly reducing time while improving coverage through exhaustive algorithmic analysis.
2Reliability
If manual test scenario generation is used, then scenarios can be created based on engineer experience, but consistency and completeness are compromised
Solution Approach 1:
The system transforms test scenario generation from a manual, experience-based process into an automated process driven by configurable parameters and algorithms. By changing the approach from human judgment to algorithmic generation with systematic parameter analysis, the system ensures consistent and complete test coverage across all scenarios while managing complexity through structured AI processing.
3Adaptability or versatility
If manual test scenario generation is used, then scenarios can be updated when requirements change, but updating is time-consuming and error-prone
Solution Approach 1:
The system implements continuous feedback loops where test execution results automatically trigger updates to the system model and user stories. When requirements change, the AI system detects these changes through feedback from test results and automatically regenerates affected test scenarios, ensuring adaptability to requirement changes while eliminating the time-consuming manual update process through automated regeneration.
4Quantity of substance
If manual test scenario generation is used, then scenarios can be created for specific cases, but scalability to large systems is difficult
Solution Approach 1:
The system segments the test scenario generation process into modular AI algorithms that can independently analyze different aspects of the system (user stories, system models, test cases). This segmentation enables scalable generation of large numbers of test scenarios by dividing the complex task into manageable algorithmic components that can process and generate scenarios systematically across large systems.
Data Source
Figure 1A~2
Figure 3
AI summary
In software testing processes, it is a system that automatically creates test scenarios (50) using a comprehensive model representing the general system behavior, along with resources such as requirement documents (10) and user stories, and constantly updates the model by analyzing past test results (70), wherein; it comprises the database where all data in the system is kept, the requirement documents (10) which include the functional and non-functional requirements of the software and are the basic inputs (A) of the system, the model creation module (20), which provides the necessary tools to model the expected software behavior from the user perspective, the software behavior model (30), which represents the behavior of the software, the interaction between sub-elements, and the general operating flows, the Al-supported test scenario creation module (40), which automatically generates test scenarios (50) using Al algorithms, using the software behavior model (30) and the requirement documents (10) provided as input (A), the test execution environment (60) where the generated test scenarios (50) are run and the test results (70) are received, the test results (70) obtained from the test execution environment (60), including the success status of the scenarios and error details, the Al-supported analysis and update module (80), which analyzes test results (70), updates and optimizes coefficients between components in the model and provides continuous learning, the updated model (90), which is a more accurate and up-to-date software behavior model, where the relationship coefficients between components are updated by the analysis and update module.