AI Cloud Application Testing Platform
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Solution Overview
Problem
Cloud applications present unique challenges in testing due to their execution on virtualized hardware, shared resources, and use of external APIs, leading to increased time, resource, and capital consumption in traditional testing methods.
Innovation Solution
A testing platform utilizing multiple artificial intelligence models to generate and optimize test cases, execute test classes, and provide analysis and recommendations for cloud applications, automating the testing process and reducing human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional testing methods are used for cloud applications, then testing can be performed, but time and resource consumption increases significantly
Solution Approach 1:
The system enables self-service testing by automatically generating test cases, optimizing test sequences, and producing test reports without requiring manual tester intervention. The AI models autonomously perform the entire testing workflow from test case generation to result analysis, eliminating the need for human testers to manually create and execute test scenarios.
Solution Approach 2:
The patent replaces manual testing mechanics with AI-based automated testing. Instead of human testers manually designing and executing test cases, the system uses machine learning models to generate, optimize, and execute test cases automatically, substituting human cognitive and manual operations with intelligent automated systems.
2Reliability
If traditional testing methods are used for cloud applications, then testing can be performed, but resource and capital consumption increases
Solution Approach 1:
The system optimizes testing parameters dynamically by using AI models to determine the most efficient test case selection, execution sequence, and resource allocation. The models analyze application characteristics and adjust testing parameters such as test depth, coverage criteria, and resource distribution to minimize computing resource consumption while maintaining testing effectiveness.
3Ease of operation
If manual testing processes are used, then testing can be performed, but automation level remains low
Solution Approach 1:
The AI-based testing platform provides universal functionality that handles multiple testing tasks automatically - from test case generation and optimization to execution and report generation. The system serves as a multi-functional automated testing suite that can adapt to different cloud applications and testing scenarios, eliminating the need for separate manual processes for each testing stage.
Data Source
AI summary
A device receives application information associated with a cloud application provided in a cloud computing environment, and utilizes a first AI model to generate test cases and test data based on the application information. The device utilizes a second AI model to generate optimized test cases and optimized test data based on the test cases and the test data, and utilizes a third AI model to generate test classes based on the optimized test cases and the optimized test data. The device executes the test classes to generate results, and utilizes a fourth AI model to generate an analysis of the results, recommendations for the cloud application based on the analysis of the results, or a code coverage report associated with the cloud application. The device automatically causes an action to be performed based on the analysis of the results, the recommendations, or the code coverage report.