GenAI Automation Script Generation for Software Testing
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Solution Overview
Problem
Software testing requires significant manual effort from developers to create and execute tests, which can be time-consuming and prone to errors.
Innovation Solution
An apparatus and method utilizing a generative artificial intelligence (GenAI) model to automatically generate testing elements and test cases based on software program requirements, reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing is used to ensure software quality, then testing reliability is improved, but development time and labor cost increase
Solution Approach 1:
The system enables self-service testing by automatically generating test cases from requirements documents using AI technology. The testing system serves itself by autonomously creating, executing, and managing test cases without requiring manual developer intervention for each test case creation, thereby maintaining reliability while reducing time loss.
Solution Approach 2:
The patent replaces the mechanical manual process of test case creation with an automated AI-based system. The generative AI model substitutes human developers' manual efforts in analyzing requirements and creating test cases, transforming the mechanical manual process into an automated intelligent system that reduces time consumption while maintaining testing quality.
2Reliability
If manual test case creation is performed to ensure comprehensive coverage, then testing completeness is improved, but developer effort and complexity increase
Solution Approach 1:
The testing system performs self-service by automatically generating comprehensive test cases from requirements documents. The system autonomously analyzes requirements, identifies test scenarios, and creates complete test cases without manual intervention, ensuring testing completeness while reducing process complexity.
Solution Approach 2:
The generative AI model acts as an intermediary between requirements documents and test cases. It mediates the transformation process by automatically interpreting requirements and generating appropriate test cases, eliminating the need for manual analysis and reducing process complexity while maintaining comprehensive test coverage.
3Productivity
If automated testing is implemented to reduce manual effort, then productivity is improved, but initial setup complexity and cost increase
Solution Approach 1:
The patent replaces manual testing mechanics with an AI-based automated system that generates and executes test cases automatically. This substitution significantly improves testing productivity by eliminating manual test case creation and execution, while the AI system manages the complexity internally, presenting a simplified interface to users.
Solution Approach 2:
The generative AI model serves as an intermediary layer that handles the complexity of test automation setup and execution. It automatically generates test cases from simple requirement inputs, shielding users from the underlying system complexity while delivering high productivity benefits through automated testing.
Data Source
AI summary
An example operation may include one or more of receiving a description of a plurality of testing elements for testing a software program and storing the software test within a storage device, generating an automation script for automating execution of the software test based on execution of a generative artificial intelligence model (GenAI) model on the plurality of testing elements and a repository of automation scripts, attaching the automation script to the software test within the storage device, and in response to a request to execute the software test, executing the plurality of testing elements within the software test based on the attached automation script.