Collaborative Mixed Reality Testing With AI-Generated Interaction Cases
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Solution Overview
Problem
Current systems lack a standardized framework for testing and debugging interactions between collaborative mixed reality objects originating from different sources, which can lead to bugs or undesired behavior.
Innovation Solution
A generative-AI-based encoder-decoder architecture is trained to generate collaborative mixed reality test cases using individual object attributes and test cases, enabling evaluation of interactions in a mixed reality environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated test case generation is implemented, then testing efficiency and coverage are improved, but system complexity increases
Solution Approach 1:
The system uses self-service by implementing automated test case generation where the encoder-decoder architecture automatically creates test cases from object attribute data without requiring manual intervention. The system feeds itself data about mixed reality objects and generates appropriate test cases autonomously, improving testing efficiency while managing complexity through automation.
Solution Approach 2:
The patent replaces manual test case creation processes with an AI-based encoder-decoder system. Instead of manually writing test cases, the system uses machine learning models to automatically generate test cases from structured data about mixed reality objects, substituting mechanical manual work with automated intelligent systems.
2Reliability
If comprehensive interaction testing is performed, then reliability of collaborative MR objects is improved, but time required for testing increases
Solution Approach 1:
The system performs preliminary action by pre-generating test cases before actual testing begins. The encoder-decoder architecture creates comprehensive test scenarios in advance based on object attributes and interaction patterns, so that when testing occurs, the test cases are already prepared, reducing overall testing time while maintaining comprehensive coverage.
Solution Approach 2:
The patent implements continuity of useful action through automated, continuous test case generation that operates alongside development processes. The system continuously generates and updates test cases as object attributes change, ensuring reliable testing without requiring discrete, time-consuming testing cycles, thereby maintaining continuous improvement of reliability.
Data Source
AI summary
A method for testing and debugging interaction of collaborative mixed reality objects is disclosed. In one embodiment, such a method includes receiving inputs including a first mixed reality object expressed by a first set of attributes, a second mixed reality object expressed by a second set of attributes, a first individual test case associated with the first mixed reality object, and a second individual test case associated with the second mixed reality object. The method automatically generates, from the inputs, a collaborative mixed reality test case to evaluate interaction of the first mixed reality object with the second mixed reality object within a collaborative mixed reality environment. In certain embodiments, a generative-AI-based encoder-decoder architecture is used to generate the collaborative mixed reality test case from the inputs. A corresponding system and computer program product are also disclosed.