LLM-Based Test Strategy Generation for Functional Feature Coverage
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Solution Overview
Problem
Manual test case generation in software development is inefficient, time-consuming, and prone to errors, struggling to keep pace with rapid development and release cycles, leading to delays and inconsistencies in software quality assurance.
Innovation Solution
Utilizing large language models (LLMs) trained on product documentation and legacy test strategies to automatically generate comprehensive test strategies and scripts, reducing manual effort and enhancing efficiency and accuracy in software testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual test case generation is used, then test coverage can be comprehensive, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces the manual mechanical process of test case generation with an AI-based automated system. The AI model analyzes product documentation, user stories, and legacy test cases to automatically generate test strategies and test cases, eliminating the need for manual creation while maintaining comprehensive coverage across all functional features.
Solution Approach 2:
The system enables self-service test generation by automatically analyzing product documentation and generating test strategies without requiring manual intervention. The AI model independently processes product specifications, user stories, and existing test cases to produce comprehensive test strategies, freeing quality engineers from repetitive manual tasks.
2Reliability
If manual test case generation is used, then quality engineers can ensure quality, but the process requires significant time and resources
Solution Approach 1:
The system performs preliminary action by automatically generating test strategies and test cases before the actual software development and testing phases. The AI model analyzes product documentation and user stories in advance to create comprehensive test strategies, enabling early quality assurance planning and reducing the time required during actual testing execution.
Solution Approach 2:
The patent replaces the time-consuming manual test planning process with an automated AI system that rapidly generates comprehensive test strategies. The AI model processes product documentation, user stories, and legacy test cases to produce quality-assured test strategies in a fraction of the time required for manual creation.
3Reliability
If comprehensive test coverage is pursued, then software quality improves, but resource consumption increases
Solution Approach 1:
The system enables self-service test generation by automatically analyzing product documentation and generating optimized test strategies without requiring extensive manual resources. The AI model independently processes specifications and creates comprehensive test coverage, reducing the need for large teams of quality engineers while maintaining high software quality standards.
Solution Approach 2:
The system changes the parameters of test generation by using AI models to automatically create test strategies with optimized coverage. The AI analyzes product documentation, user stories, and legacy test cases to generate test strategies that achieve comprehensive coverage with fewer resources, optimizing the balance between quality and resource consumption.
4Adaptability or versatility
If test strategies are generated manually, then customization is possible, but consistency and standardization are difficult to achieve
Solution Approach 1:
The system achieves universality by creating a standardized AI-driven framework that generates consistent test strategies across multiple projects and teams. The AI model processes product documentation, user stories, and legacy test cases using统一的 methodologies and templates, ensuring that all generated test strategies follow consistent standards while remaining adaptable to specific product requirements.
Solution Approach 2:
The system changes the parameters of test strategy generation by using AI to automatically apply consistent methodologies and templates across all projects. The AI model maintains stability in test strategy composition by following standardized processes while adapting to specific product contexts, achieving both consistency and customization simultaneously.
Data Source
AI summary
Methods, systems, and apparatus, including medium-encoded computer program products for executing a test strategy, include: receiving a request to generate a test strategy for a new feature of a software solution, wherein the received request includes a specification of the new feature; generating the test strategy by executing a large language model to automatically generate one or more test strategies based on technical documentation for the software solution, a test strategy template, and legacy test strategies defined for the software solution; and executing the generated test strategy for the new feature of the software solution to obtain output data, the output data including monitoring data for performance of the new feature, and the performance of the new feature being determined based on criteria of the generated test strategy.