Model Element Relevance Scoring for Test Reduction
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Solution Overview
Problem
Large test models are difficult to debug, time-consuming to execute, and contain redundant information, making them hard to share and manage, especially in cloud computing environments.
Innovation Solution
A system that calculates relevance scores for model elements, reduces the model based on these scores and a threshold reduction score, and evaluates the reduced model against a test case to reproduce expected behavior, thereby generating a smaller, efficient test model for debugging and modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the test model includes all model elements to ensure complete coverage, then the testing coverage is improved, but the model size and execution time increase
Solution Approach 1:
The patent extracts and removes redundant model elements from the test model while preserving essential coverage. The system identifies and eliminates unnecessary model elements that do not contribute to test coverage, thereby reducing model size and execution time while maintaining adequate testing coverage through intelligent selection of critical elements.
Solution Approach 2:
The patent changes the parameter of model element selection from including all elements to including only relevant elements. By dynamically adjusting which model elements are included based on their relevance to the testing objectives, the system optimizes the balance between coverage and execution efficiency.
2Reliability
If the test model includes all model elements to ensure complete coverage, then the testing coverage is improved, but the model complexity increases
Solution Approach 1:
The patent extracts and removes redundant model elements from the test model while preserving essential coverage. The system identifies and eliminates unnecessary model elements that do not contribute to test coverage, thereby reducing model size and execution time while maintaining adequate testing coverage through intelligent selection of critical elements.
Solution Approach 2:
The patent segments the model elements into relevant and irrelevant categories, processing only the necessary segments for testing. This segmentation approach divides the complex model into manageable portions, reducing overall model complexity while preserving essential testing functionality.
3Loss of time
If the model size is reduced to improve execution speed, then the execution time is reduced, but the testing coverage may be compromised
Solution Approach 1:
The patent changes the parameter of model element selection from including all elements to including only relevant elements. By dynamically adjusting which model elements are included based on their relevance to the testing objectives, the system optimizes the balance between coverage and execution efficiency.
Solution Approach 2:
The patent applies different quality standards to different model elements, prioritizing retention of elements with high testing value while allowing removal of elements with low contribution. This local quality approach ensures that critical model elements are preserved for coverage while non-critical elements are removed to reduce execution time.
4Loss of substance
If the model is reduced to a smaller size, then the storage and network traffic are reduced, but the model may lose important information
Solution Approach 1:
The patent extracts and removes redundant model elements from the test model while preserving essential coverage. The system identifies and eliminates unnecessary model elements that do not contribute to test coverage, thereby reducing model size and execution time while maintaining adequate testing coverage through intelligent selection of critical elements.
Solution Approach 2:
The patent changes the parameter of model element selection from including all elements to including only relevant elements. By dynamically adjusting which model elements are included based on their relevance to the testing objectives, the system optimizes the balance between coverage and execution efficiency.
Data Source
AI summary
A method includes receiving, via a processor, a model, a test case, and one or more suspect elements. The method includes calculating, via the processor, a relevance score for each model element of the model based on the one or more suspect elements. The method includes setting, via the processor, a threshold reduction score. The method includes generating, via the processor, a reduced model by reducing the model based on the relevance scores and the threshold reduction score. The method includes evaluating, via the processor, the reduced model based on the test case. The method includes detecting, via the processor, the reduced model reproduces an expected behavior. The method includes outputting, via the processor, a result model in response to detecting the reduced model reproduces the expected behavior. The method includes modifying, via the processor, an application associated with the model based on the reduced model.