Software Model Comparison Using Principal Component Analysis
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Solution Overview
Problem
Comparing software models is challenging when the importance of test metrics is unknown, as existing methods require selecting the most appropriate metrics for evaluation, which can be unclear.
Innovation Solution
A computer-implemented method using dimension reduction techniques, specifically principal component analysis, to generate principal components from metric data of both current and new software versions, determining control limits based on these components, and comparing the mean values to assess performance within established limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple test metrics are used to evaluate software model performance, then measurement precision is improved, but device complexity increases due to the difficulty of selecting and comparing multiple metrics when their importance is unknown
Solution Approach 1:
The patent combines multiple test metrics into a single composite score using principal component analysis. The system executes multiple tests on software models, collects various metrics (accuracy, precision, recall, etc.), and merges them into one aggregated performance indicator that allows direct comparison between model versions without requiring users to weigh individual metrics.
Solution Approach 2:
The patent introduces principal component analysis as an intermediary mathematical technique that transforms multiple correlated metrics into independent principal components. This intermediary process automatically determines the relative importance of each metric through variance analysis, eliminating the need for users to manually select or weight metrics while preserving measurement precision.
2Device complexity
If dimension reduction technique is applied to aggregate metrics, then device complexity is reduced, but loss of information occurs when combining multiple metrics into a single principal component
Solution Approach 1:
The patent transforms the parameter representation from multiple individual metrics to a single principal component parameter. By changing the parameter space through mathematical transformation, the system reduces complexity while retaining the essential performance information that distinguishes between model versions, as the principal component captures the maximum variance in the original metric set.
Solution Approach 2:
The patent uses partial action by selecting only the most significant principal components (typically the first one) that capture the majority of performance variation. This partial approach retains sufficient information for practical model comparison while discarding redundant or less important metric variations, achieving an optimal balance between information retention and complexity reduction.
Data Source
AI summary
A method includes generating first metric data by executing a test suite on a first version of a software model; generating second metric data by executing the test suite on a second version of the software model; generating first and second version principal components that represent the first and second metric data by executing a dimension reduction technique on the first and second metric data; determining at least one control limit based on at least one of the first version principal components; determining a mean value of one of the second version principal components; determining whether the second version of the software model performs within at least one control limit by comparing the mean value to the at least one control limit; and notifying a user of the results of the determining whether the second version of the software model performs within the at least one control limit.