Hybrid AI Test Case Generation for Complex Software Scenarios
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Solution Overview
Problem
The challenge of generating complex and comprehensive test cases in software testing environments is exacerbated by the increasing complexity of software and the need for efficient, adaptable, and expedient test case generation to meet evolving organizational requirements.
Innovation Solution
A system utilizing a hybrid artificial intelligence model, comprising a hybrid transformer model and an AI acceleration unit, processes user inputs to generate test cases efficiently, incorporating preprocessing and post-processing steps to refine and format the output for compatibility with testing workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional test case generation methods are used, then test cases can be generated, but the process is time-consuming and inefficient for complex software systems
Solution Approach 1:
The patent replaces traditional mechanical test case generation methods with an AI-based system that uses transformer models and neural networks to automatically generate test cases from natural language descriptions, significantly reducing the time required while improving productivity
Solution Approach 2:
The system enables self-service test case generation where users can input high-level test scenarios in natural language and the AI system automatically generates comprehensive test cases without requiring manual intervention in the generation process, thereby reducing time loss and increasing productivity
2Reliability
If comprehensive test cases are generated to cover all functionalities, then test coverage improves, but the complexity and time required for generation increases
Solution Approach 1:
The patent uses AI models including transformer models and neural networks to automatically generate comprehensive test cases with high reliability, replacing complex manual processes and reducing the perceived complexity through automation
Solution Approach 2:
The system segments the test case generation process into distinct stages including preprocessing, model generation, and postprocessing, making the overall complex task manageable and automatable while ensuring comprehensive coverage through structured approach
3Productivity
If test cases are generated quickly, then productivity improves, but the quality and comprehensiveness of test cases may deteriorate
Solution Approach 1:
The patent implements continuous refinement through iterative processes where generated test cases are validated and refined through multiple stages including preprocessing, model generation, and postprocessing, ensuring high quality while maintaining speed through parallel processing and optimized AI models
Solution Approach 2:
The system incorporates feedback mechanisms where generated test cases are validated against predefined criteria and user feedback is used to refine the AI models, ensuring continuous improvement in test case quality while maintaining high generation speed
Data Source
AI summary
A system is provided for automated test case generation using a hybrid artificial intelligence model. In particular, the system may comprise an automated test generator (“ATG”) that may automatically generate test cases or scenarios using one or more artificial intelligence (“AI”) models. In this regard, a user may input a high level test scenario into the ATG. Subsequently, the ATG may use a hybrid model (e.g., a model combining multiple transformer models) to generate complex and comprehensive test cases based on the user input. In some embodiments, the ATG may use an AI accelerator processing unit to increase the speed of the test case generation and refinement processes. In this way, the system provides an expedient, efficient way to generate complex test cases for software testing applications.