Graph-Augmented AI Test Case Generation for Complex UI Coverage
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Solution Overview
Problem
Existing Gen AI models struggle with generating accurate and complete test cases for complex user interfaces due to challenges such as complexity of UI elements, dynamic content, state management, edge cases, context awareness, performance, validation, and integration with existing tools, leading to incomplete coverage and inaccurate outcomes.
Innovation Solution
A system and method that preprocesses raw data to represent complex elements and their intrinsic relationships, processes it to guide Gen AI models for evaluation, and post-processes the output to eliminate redundancy, using a Retrieval-Augmented Generation technique to enhance the accuracy and relevance of test case generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If Gen AI models are used to generate test cases for complex user interfaces, then test case generation can be automated, but the accuracy and completeness of generated test cases deteriorates due to challenges such as complexity of UI elements, dynamic content, state management, edge cases, context awareness
Solution Approach 1:
The patent segments the test case generation process into multiple stages: data preprocessing to represent complex elements and their relationships, retrieval of relevant information from data sources, generation of test cases by the Gen AI model, and post-processing to eliminate redundancy. This segmentation allows each stage to address specific challenges independently, improving overall reliability while maintaining automation.
Solution Approach 2:
The patent introduces an intermediary processing layer between the Gen AI model and the final test cases. This intermediary includes data preprocessing that transforms raw data into a format suitable for AI processing, and post-processing that refines the generated test cases. This intermediary layer mediates the complexity of UI elements and dynamic content, ensuring accurate and complete test case generation.
2Speed
If Gen AI models process raw data directly, then processing speed is fast, but the quality of generated test cases deteriorates due to lack of structured information and context
Solution Approach 1:
The patent applies preliminary action by preprocessing raw data before it is fed to the Gen AI model. This preprocessing step structures the data to represent complex elements and their intrinsic relationships, preparing it in advance for more accurate processing. This preliminary organization of information enables the AI model to generate higher quality test cases while maintaining efficient processing speeds.
Solution Approach 2:
The patent introduces data preprocessing as an intermediary step between raw data input and AI model processing. This intermediary layer transforms unstructured raw data into structured representations that capture complex elements and relationships, bridging the gap between fast processing and high quality output.
3Productivity
If Gen AI models generate test cases without post-processing, then generation speed is high, but redundancy and inefficiency increase
Solution Approach 1:
The patent extracts and removes redundant information from the generated test cases through post-processing. This extraction of unnecessary content eliminates redundancy while preserving the essential test case information, thereby reducing computer resources consumption and improving efficiency without significantly impacting generation speed.
Solution Approach 2:
The post-processing stage discards redundant and inefficient test case elements while recovering and preserving the valuable test information. This selective discarding and recovering of data optimizes resource utilization and improves the efficiency of test case generation.
Data Source
AI summary
The present disclosure relates to a technique for addressing an issue to be resolved associated with an electronic document. The method discloses accessing an actionable portion associated with a particular knowledge domain of the electronic document and associated context. Further, retrieve data from data sources to provide additional information related to the particular knowledge domain and the associated context. Then structuring the retrieved data to produce a subset of organized data and determine the issue to be resolved related to the electronic document. Further, generate data elements associated with the issue to be resolved and map dependency relationships between data elements. Also, determine test goals associated with the issue to be resolved based on the dependency relationships. Thereafter, determine corresponding test cases associated with resolution and determines actionable test steps related to the issue to be resolved based on the corresponding test cases associated with the electronic document.