Parallel Testing System for Optimized Sample Dataset Selection
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Solution Overview
Problem
Current comparison testing methods involving large datasets are inefficient in terms of time and resource utilization, as they often require processing the entire dataset, which can lead to diminishing returns and misrepresentation of data populations, especially when updating applications from legacy to new versions.
Innovation Solution
The implementation of a sampling data format using combinatorial strings to select a representative sample dataset, optimizing the number and type of test data for inclusion, and utilizing a parallel testing and reporting tool to efficiently process and compare outputs between legacy and new applications, thereby improving processing speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire dataset is processed for comparison testing, then comprehensive testing coverage is achieved, but processing time and resource utilization increase significantly
Solution Approach 1:
The patent extracts a representative subset of data from the entire dataset by identifying and removing duplicate or redundant data elements. This sampling approach maintains testing coverage while reducing the volume of data that requires processing, thereby resolving the contradiction between comprehensive testing and processing time.
Solution Approach 2:
Instead of processing the entire dataset, the patent applies partial action by processing only a carefully selected sample that represents the whole population. This partial processing achieves sufficient testing coverage without the excessive time cost of processing all data elements.
2Measurement precision
If a large sample dataset is used for parallel testing, then testing accuracy is improved, but resource utilization and processing efficiency deteriorate
Solution Approach 1:
The patent extracts essential test cases from the large dataset by identifying and removing redundant elements. This extraction process maintains testing accuracy by preserving representative data while reducing the overall dataset size, thereby improving processing efficiency without sacrificing test validity.
Solution Approach 2:
The patent changes the parameter of dataset size by transforming a large dataset into a optimized sample dataset. This parameter change maintains the statistical representativeness needed for accurate testing while reducing the computational burden, thus resolving the contradiction between accuracy and efficiency.
3Reliability
If redundant data is included in the test dataset, then data population representation is maintained, but processing time and resource consumption increase
Solution Approach 1:
The patent systematically identifies and extracts redundant data elements from the dataset while preserving the essential characteristics that represent the data population. This extraction eliminates unnecessary resource consumption while maintaining reliable data representation through the remaining unique test cases.
Solution Approach 2:
The patent discards redundant data elements that do not contribute additional value to testing coverage. By recovering and retaining only the essential unique data points, the system maintains data population representation while significantly reducing resource consumption during processing.
Data Source
AI summary
An optimized test data selection strategy references a sampling file that identifies data attributes that serve as the basis of the test data selection strategy. By analyzing fields and the corresponding field values of the sample imprint, a total number of test data selected for inclusion into a sample dataset is reduced. The test data selection strategy provides an efficient methodology for implementing a data comparison testing process.