Multiple Comparison Correction for Feature Experiment Metrics
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Solution Overview
Problem
The complexity of software applications makes it difficult to accurately measure the impact of updates on operational performance and user experience, leading to inefficient and inaccurate testing of new features across different user groups.
Innovation Solution
A system architecture that includes an application monitoring and configuration server, end user systems, and an application developer system, which uses feature treatment logic to selectively enable or disable application features, track events, and perform multiple comparison correction to attribute metric changes to specific feature treatments, thereby improving the accuracy of feature deployment testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple metrics are measured simultaneously during deployment of new application features, then the ability to comprehensively evaluate feature impact is improved, but the potential for error in analysis increases to an unacceptable level
Solution Approach 1:
The patent segments the analysis process by applying multiple comparison correction methods (such as Bonferroni correction, False Discovery Rate control) to adjust significance thresholds for each metric. This segmentation of the statistical analysis allows comprehensive evaluation of multiple metrics while maintaining controlled error rates through adjusted p-value thresholds.
Solution Approach 2:
The patent introduces an intermediary statistical correction layer between raw metric measurements and final conclusions. By using multiple comparison correction as an intermediary processing step, the system can evaluate multiple metrics simultaneously while the correction mechanism mediates the error accumulation problem by adjusting significance levels.
2Adaptability or versatility
If the complexity of software applications increases with more user interface elements and functional features, then the capability and versatility of the application is improved, but the difficulty of measuring the impact of updates on operational performance increases
Solution Approach 1:
The patent segments the complex application into distinct feature treatments and associated metrics. By breaking down the complex system into measurable feature components and their corresponding performance metrics, the patent enables targeted measurement of feature impact even within highly complex applications with multiple interface elements and functional features.
Solution Approach 2:
The patent changes the measurement parameters by using statistical significance thresholds and multiple comparison correction methods. This parameter transformation allows the system to cut through the complexity of multiple application features and identify which specific features have statistically significant impacts on performance metrics.
3Productivity
If traditional testing methods are used without multiple comparison correction, then the simplicity and speed of testing is maintained, but the accuracy and reliability of feature deployment testing becomes insufficient
Solution Approach 1:
The patent applies preliminary statistical correction factors before drawing conclusions from metric measurements. By pre-calculating adjustment factors for multiple comparisons and applying them to significance thresholds beforehand, the system maintains efficient testing workflows while ensuring accurate results through pre-established statistical corrections.
Data Source
AI summary
A method and apparatus for configurable application feature experiments is described. The method may include receiving a set of metrics to be collected after a feature treatment is deployed to configurable applications executed by a plurality of end user systems, a significance threshold for detection of feature treatment impact on one or more metrics within the set of metrics, and a request to perform multiple comparison correction when detecting the feature treatment impact. The method may also include receiving, from the configurable applications, event messages that include metric values associated with the set of metrics. Further, the method may also include performing a statistical analysis of the metric values to determine whether the feature treatment caused a statistically significant change in values associated with one or more of the metrics, where the statistical analysis adjusts one or more parameters used to perform the statistical analysis based on a multiple comparison correction analysis.