Mixed Reality Simulation for Software Testing Context
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Solution Overview
Problem
Software development testing often fails to simulate all potential deployment scenarios, leading to unforeseen errors and increased maintenance costs due to incomplete testing.
Innovation Solution
A method and system that generate a mixed reality simulation of a software application's deployment environment using machine learning and digital twin models to create a virtual reality environment, allowing users to interact and gather contextual data for comprehensive testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional software testing methods are used, then testing can be completed with simple tools and processes, but the testing does not reflect all deployment situations and leads to unforeseen errors
Solution Approach 1:
The patent creates a virtual copy (digital twin) of the deployment environment that replicates real-world conditions, allowing comprehensive testing without needing multiple physical test environments. This virtual environment copying enables complete scenario coverage while avoiding the complexity of managing numerous physical test setups
Solution Approach 2:
The system performs preliminary simulation of deployment scenarios before actual software deployment. By pre-simulating various deployment situations in the virtual environment, the system identifies potential errors in advance, ensuring more complete testing while maintaining manageable complexity through automated scenario generation
2Reliability
If comprehensive testing of all deployment scenarios is performed, then software reliability improves, but testing time and resources increase
Solution Approach 1:
The system uses periodic simulation cycles where the virtual environment automatically executes predefined deployment scenarios at scheduled intervals. This periodic testing approach ensures comprehensive scenario coverage over time while optimizing resource utilization through automated, repetitive test execution rather than manual continuous testing
Solution Approach 2:
The virtual deployment environment performs self-testing by automatically executing test scenarios and generating test results without requiring constant human intervention. The system autonomously manages test case execution, data collection, and analysis, improving software quality through comprehensive testing while reducing the time investment required from human testers
Data Source
AI summary
Mechanisms are provided to generate a test dataset for software application development. A baseline mixed reality (MR) environment simulation of an existing process is generated that models applications of an application landscape. A requirement for an application that is to be developed is received and a MR component model is generated to represent the application based on a machine learning computer model processing of the requirements data structure and a knowledge corpus. The MR component model modifies the baseline MR environment simulation, and executes the modified MR environment simulation to simulate the modified MR environment simulation. The simulation is monitored for user input specifying a contextual scenario and context data is collected from the MR component models of the modified MR environment simulation. A test dataset for testing a coded version of the SUD application is generated based on the contextual scenario and the collected data.