Virtual Testing Environment for Dynamic Production Simulation
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Solution Overview
Problem
Existing software testing systems rely on predefined test data and scenarios, which may not accurately represent real-time production environments, leading to inefficient testing and lack of insight into product behavior under varying conditions.
Innovation Solution
A method and system for dynamically testing products and processes in a virtual testing environment by retrieving real-time production data, generating scenarios based on event sequencing, and recreating virtual products and processes for accurate simulation, allowing for selective actions like play, pause, and modification during testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If testing is performed in the production environment with real-time feedback, then testing accuracy is improved, but product disruption and complexity increase
Solution Approach 1:
The patent creates a virtual copy of the production environment including virtual products, virtual processes, and virtual data repositories. This virtual testing environment replicates production scenarios without disrupting actual operations, allowing accurate testing while avoiding the complexity and disruption of direct production environment testing.
Solution Approach 2:
The virtual testing environment acts as an intermediary between the actual product and the testing process. By routing tests through this virtual layer, the system achieves testing accuracy without directly interfering with production operations, thus reducing complexity and disruption.
2Ease of operation
If predefined test data and scenarios are used, then testing process is simplified, but testing coverage and accuracy in production environments deteriorate
Solution Approach 1:
The system dynamically generates test data and scenarios by capturing actual production events and sequences. Instead of using static predefined test data, the testing approach adapts to real production conditions, improving coverage accuracy while maintaining process simplicity through automated dynamic generation.
Solution Approach 2:
The system incorporates feedback loops where test results from the virtual environment inform and refine subsequent test scenarios. This continuous feedback mechanism ensures test coverage accuracy improves over time while the process remains simple through automation.
3Stability of the object's composition
If continuous testing with same test data is performed, then testing consistency is maintained, but testing efficiency decreases due to lack of scenario diversity
Solution Approach 1:
The system implements periodic refreshment of test scenarios by continuously capturing new production events and sequences. This periodic update mechanism introduces scenario diversity while maintaining consistency through structured test frameworks, thereby improving testing efficiency without sacrificing consistency.
Solution Approach 2:
The system performs preliminary capture and analysis of production events to pre-generate diverse test scenarios before actual testing begins. This preliminary action ensures both consistency through structured preparation and efficiency through scenario diversity, avoiding the need for continuous same-scenario testing.
4Measurement precision
If real-time production data is captured and replicated, then testing accuracy is improved, but data processing complexity and time increase
Solution Approach 1:
The system performs preliminary capture and structuring of production data as events occur in real-time, organizing them into reusable test scenarios before testing begins. This preliminary action reduces the processing time during actual testing while maintaining testing accuracy through comprehensive data capture.
Solution Approach 2:
The system creates virtual copies of production data in structured formats suitable for testing. This copying process transforms raw production data into optimized test data that maintains accuracy requirements while reducing processing complexity and time during test execution.
Data Source
AI summary
Systems and methods for dynamically testing product and process of production environment in virtual testing environment are disclosed. A testing system may retrieve production data related to each production activity corresponding to events executing in real-time. Each production activity is related to product and process of production environment. Further, testing system generates each scenario of production environment in virtual testing environment in real-time, based on sequencing of each event and creates, in the virtual testing environment, virtual process and virtual product corresponding to process and product of production environment, based on at least the events, generated scenarios, and historical data. Finally, testing system re-plays each production activity in virtual testing environment for testing virtual process and virtual product. The method may comprise testing using real-time scenarios of production environment, allowing a user to view testing process and perform selective actions while testing.