Natural-Language UI Page Testing Code With Readable Action Mapping
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Solution Overview
Problem
Existing UI automated testing tools generate low-quality, poorly readable, and maintainable test code that requires manual secondary modification, necessitating testers with coding skills and time-consuming assembly of test steps.
Innovation Solution
Employing a machine learning model to generate UI automated testing code based on natural language inputs, utilizing a repository of standardized actions and associations to improve code quality and readability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional UI automated testing tools are used to generate test code, then automation is achieved, but the generated code has low quality and poor readability
Solution Approach 1:
The patent introduces an intermediary processing system that receives natural language test requirements, analyzes them, and generates standardized test code. This intermediary layer (the processing system with its analysis and generation modules) mediates between the user's intent and the final test code, ensuring high quality and readability while maintaining automation.
Solution Approach 2:
The patent replaces traditional mechanical code generation mechanisms with an intelligent processing system that uses natural language understanding and analysis. Instead of relying on rigid, pre-programmed code generation rules, the system substitutes a more flexible, intelligent approach that can comprehend and translate natural language requirements into high-quality test code.
2Productivity
If traditional test code generation methods are used, then testing can be performed, but manual secondary modification is required
Solution Approach 1:
The patent implements a self-service mechanism where the processing system automatically analyzes natural language requirements and generates complete, ready-to-use test code without requiring manual intervention or secondary modification. The system serves itself by internally handling the translation from requirements to executable test code, eliminating the need for testers to manually adjust the generated code.
Solution Approach 2:
The patent performs preliminary analysis and processing of test requirements before code generation. By预先 analyzing the natural language input and preparing the test logic in advance, the system ensures that the generated code is complete and ready for immediate execution, eliminating the need for subsequent manual modifications.
3Reliability
If testers use traditional code generation tools, then test execution is possible, but coding skills and time are required for assembly
Solution Approach 1:
The patent performs preliminary analysis of test requirements and pre-processes the logic before code generation. This preliminary action ensures that all necessary test steps are properly assembled and ready for execution, eliminating the time-consuming manual assembly process while maintaining testing reliability.
Solution Approach 2:
The patent replaces the mechanical process of manual code assembly with an intelligent processing system that automatically generates complete, executable test code. This substitution eliminates the need for testers to spend time assembling code pieces, while the systematic approach ensures reliable test execution.
Data Source
AI summary
A method, an apparatus, a device, and a medium for processing a user page are provided. In the method, a processing request represented in a natural language is received, and the processing request is configured to perform a first action on a first page element in a group of page elements in the user page. A repository is obtained. The repository includes a group of actions performed on the group of page elements and an association between code data for performing the group of actions, and the group of actions includes the first action. Based on the processing request and the repository, first code data for performing the first action on the first page element is determined.