LLM-Orchestrated Test Script Creation for UI Workflow Consistency
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Solution Overview
Problem
Manual creation of test scripts for software application updates is time-consuming and resource-intensive, heavily dependent on the expertise of the creator, and lacks efficiency and consistency.
Innovation Solution
A machine learning model-based approach is used to create and configure test scripts by orchestrating application functions and controls, leveraging a large language model to suggest steps and interface elements, reducing reliance on individual expertise and improving script quality and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual creation of test scripts is used, then script quality can be high when created by experts, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system enables automated self-service test script generation through machine learning models that autonomously analyze application functions, identify test scenarios, and create executable test scripts without requiring manual intervention from testing experts, thereby reducing time loss while maintaining quality through algorithmic consistency
Solution Approach 2:
The patent replaces the mechanical manual process of expert test script creation with an automated machine learning-based system that uses natural language processing and generative models to transform application documentation into test scripts, eliminating the time-consuming manual mechanics while preserving quality through systematic analysis
2Adaptability or versatility
If manual creation of test scripts is used, then scripts can be customized according to expert knowledge, but the process lacks efficiency and consistency
Solution Approach 1:
The system achieves adaptability through parameter changes by allowing configuration of test script generation parameters such as test coverage criteria, execution priorities, and specific test scenarios to be customized according to different application types and testing requirements, while maintaining high productivity through automated processing of these parameters
Solution Approach 2:
The machine learning model provides universal test script generation capability that can adapt to multiple application types and testing scenarios through a single unified system, enabling both customization for specific applications and consistent efficient processing across diverse testing needs
3Reliability
If expert knowledge is heavily relied upon for script creation, then script quality can be maintained, but the process becomes resource-intensive and dependent on individual expertise
Solution Approach 1:
The system embeds expert knowledge within the machine learning model itself, enabling the system to autonomously generate high-quality test scripts without requiring external expert resources, thereby reducing device complexity and resource dependency while maintaining consistent quality through the trained model's systematic approach
Solution Approach 2:
The system performs preliminary action by pre-training the machine learning model on extensive test case data and application documentation during an offline phase, so that during actual script generation, the model already possesses embedded expert knowledge and can operate independently without requiring real-time expert intervention
Data Source
AI summary
In some implementations, there is provided a computer-implemented method that includes determining, via the application function processor, a first suggested step for the test script, the first suggested step for the test script being a step of a workflow for the application obtained from the machine learning model; determining, via the orchestrating model, a first user interface control from a plurality of user interface controls stored within the object repository, and at least a first datum to be entered into a first input field, wherein the first user interface control is associated with a user interface location; and providing, by the orchestrating model and to the user interface, the first suggested step for the test script and a first locator associated with the first user interface control and entering at least the first datum into the first input field. Related systems, methods, and articles of manufacture are also disclosed.