ML Proximity Analysis for Software Population Testing
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Solution Overview
Problem
Current mechanisms for comparing pre-production and post-production populations for software testing are inefficient, biased, and limited in analyzing multiple user characteristics, leading to potential issues in software performance across diverse user populations.
Innovation Solution
A data processing system utilizing a machine-learning model to analyze telemetry and feedback data from pre-production and post-production populations, calculating proximity scores based on key characteristics to determine the similarity between these populations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a small group of users is selected for software testing, then the testing process becomes manageable and efficient, but the representation of the larger user population becomes insufficient
Solution Approach 1:
The patent introduces an intermediary system that uses machine learning models to analyze and compare the characteristics of the test population with the broader user population. This intermediary analysis layer enables accurate population representation assessment without requiring the test group to be as large as the entire user base, thus maintaining testing efficiency while improving measurement precision.
2Device complexity
If traditional comparison methods are used to analyze user population characteristics, then the process is simpler, but the ability to identify key differences and similarities is limited
Solution Approach 1:
The patent replaces traditional mechanical comparison methods with machine learning-based analysis. The system uses ML models to automatically identify patterns, similarities, and differences between user populations, substituting manual or simple algorithmic comparison with intelligent systems that provide deeper insights while maintaining reasonable complexity through automated processing.
3Adaptability or versatility
If multiple user characteristics are analyzed simultaneously, then the comprehensiveness of the analysis improves, but the complexity of the analysis process increases
Solution Approach 1:
The patent segments the analysis process into distinct components: data collection from multiple sources, feature extraction from various user characteristics, model training on segmented datasets, and result aggregation. This segmentation allows the system to handle multiple user characteristics comprehensively while managing complexity through modular processing steps, where each segment can be independently optimized and analyzed.
Data Source
AI summary
A method and system for analyzing a proximity between a first user population and a second user population includes receiving a request to perform a proximity analysis between the first user population and the second user population, accessing data related to the first user population and the second user population, providing the data related to the first user population and the second user population as input to a machine-learning (ML) model for analyzing the data to determine the proximity between the first user population and the second user population, receiving from the ML model as an output at least one of a composite proximity score between the first user population and the second user population, and providing display data relating to the output to a visualization mechanism for display. The composite proximity score may be calculated based on multiple characteristics and/or comparison metrics.