AI Autonomous Continuous Testing Platform for Enterprise Apps
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Solution Overview
Problem
Current continuous testing methodologies lack efficiency and automation, requiring significant manual intervention and time in generating and executing test scripts, especially when dealing with diverse enterprise applications like SAP, Oracle, and Salesforce, which hampers the ability to test continuously and autonomously.
Innovation Solution
An artificial intelligence-based autonomous continuous testing platform that utilizes AI and machine learning to generate and execute test scripts autonomously, employing configuration mining, system-specific models, and deep learning to identify and extract configuration information, generate user journeys, and create test scripts without user input, integrating with cloud-based systems for scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual methods are used to generate and execute test scripts, then testing can be performed with simple tools, but test creation time and manual intervention requirements increase significantly
Solution Approach 1:
The testing system performs self-service by automatically generating test scripts, executing tests, and creating reports without requiring manual intervention. The system autonomously navigates user journeys, identifies test scenarios, and generates comprehensive test scripts, eliminating the need for manual test creation and execution while significantly reducing test creation time.
Solution Approach 2:
The patent replaces manual mechanical testing processes with an automated AI-based system. Instead of manually creating and executing test scripts, the system uses intelligent agents that automatically perform testing tasks, substituting human effort with automated computational processes that are faster and more efficient.
2Productivity
If traditional testing methodologies are used, then testing processes are simple to implement, but testing frequency and continuous validation capability are limited
Solution Approach 1:
The system enables continuous testing by automatically executing test scripts across multiple user journeys and applications without interruption. The intelligent agents continuously generate, execute, and validate tests, providing ongoing validation of system functionality rather than periodic manual testing, thus significantly increasing testing frequency.
Solution Approach 2:
The testing platform is designed to be universal and multi-functional, capable of testing diverse enterprise applications including SAP, Oracle, and Salesforce through a single unified system. The intelligent agents can adapt to different applications and user journeys, providing comprehensive testing coverage across multiple platforms without requiring separate testing tools for each application.
3Reliability
If comprehensive testing coverage is achieved across diverse enterprise applications, then testing thoroughness improves, but manual effort and time requirements increase
Solution Approach 1:
The system autonomously performs comprehensive testing across diverse applications without requiring manual operation. The intelligent agents automatically navigate complex user journeys, identify test scenarios, and execute tests thoroughly across SAP, Oracle, Salesforce, and other enterprise applications, maintaining high testing thoroughness while requiring minimal human intervention.
Solution Approach 2:
The patent introduces intelligent agents as intermediaries between the testing platform and diverse enterprise applications. These agents act as mediators that can interact with multiple application types uniformly, translating complex application-specific testing requirements into standardized test execution processes, thereby maintaining thoroughness while simplifying operational complexity.
Data Source
AI summary
Obtaining a configuration hook. Obtaining a base configuration of a remote system using the configuration hook. Obtaining a system-specific model associated with the configuration hook and the remote system. Obtaining one or more pre-built test accelerators. Obtaining a deep machine learning model. Generating, based on the base configuration, the system-specific model, the one or more pre-built test accelerators and the deep machine learning model, a custom configuration model. Generating a plurality of user journeys to be autonomously tested. Generating, based on the custom configuration model and the plurality of user journeys to be autonomously tested, a plurality of autonomous test scripts. Autonomously pre-configuring at run-time the plurality of autonomous test scripts. Autonomously executing the plurality of autonomous test scripts. Generating, based on the autonomously executed plurality of autonomous test scripts, one or more autonomous test reports. Presenting the one or more autonomous test reports.