Predicting Retail Software Quality via Covariate Reweighting
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Solution Overview
Problem
Evaluating pre-release software quality is challenging due to biased assessments from a smaller population of software enthusiasts, whose hardware and usage patterns differ from the retail audience, making it difficult to infer software quality for the broader market.
Innovation Solution
A method and system that utilize telemetry data from both preview and retail device populations to identify covariates impacting software quality, applying coarsened exact matching to reweight quality metrics and generate a predicted quality metric for the retail audience, ensuring more accurate assessments of software readiness for deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-release software is evaluated by a small group of software enthusiasts, then evaluation can be conducted quickly and with limited resources, but the quality assessment becomes biased and not representative of the retail audience
Solution Approach 1:
The patent introduces an intermediary statistical model that acts as a bridge between the biased evaluator population and the target retail audience. The model uses covariates and weighting mechanisms to translate evaluations from the enthusiast group into predictions that accurately represent the broader retail population, resolving the contradiction between fast evaluation and accurate quality assessment.
Solution Approach 2:
The patent transforms the evaluation data by changing key parameters through statistical adjustments. By modifying the weight of each evaluation based on covariate matching between evaluator and retail audience characteristics, the system converts biased quick evaluations into accurate quality predictions without requiring a large representative sample.
2Loss of information
If software enthusiasts use pre-release software to explore new features, then feature feedback is obtained, but usage patterns differ from retail users leading to skewed quality metrics
Solution Approach 1:
The patent adjusts the usage pattern parameters by introducing weighting factors that account for differences between enthusiast and retail user behaviors. The system modifies the contribution of each evaluation based on how closely the evaluator's usage patterns match the target retail audience, preserving feature feedback while correcting usage pattern biases.
3Reliability
If hardware used by software enthusiasts is better than retail audience hardware, then software performance is improved during testing, but quality metrics do not reflect performance on typical retail hardware
Solution Approach 1:
The patent introduces hardware characteristic parameters as covariates in the statistical model. By measuring and comparing hardware specifications between enthusiast and retail audiences, the system adjusts the weight of evaluations from enthusiasts with superior hardware, scaling their performance metrics to reflect expected performance on typical retail hardware configurations.
4Measurement precision
If coarsened exact matching is applied to reweight quality metrics, then prediction accuracy for retail audience improves, but computational complexity increases
Solution Approach 1:
The patent segments the population into discrete groups based on covariate characteristics using coarsened exact matching. By dividing both the evaluator population and retail audience into matched segments, the system applies reweighting at the segment level rather than individually, reducing computational complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent transforms continuous covariate variables into discrete segments through coarsening, which simplifies the matching process. This parameter transformation reduces the computational burden of exact matching while preserving the essential differences between population groups, achieving a balance between accuracy and complexity.
Data Source
AI summary
Systems and methods directed to generating a predicted quality metric are provided. Telemetry data may be received from a from a first group of devices executing first software. A quality metric for the first software may be generated based on the first telemetry data. Telemetry data from a second group of devices may be received, where the second group of devices is different from the first group of devices. Covariates impacting the quality metric based on features included in the first telemetry data and the second telemetry data may be identified, and a coarsened exact matching process may be performed utilizing the identified covariates to generate a predicted quality metric for the first software based on the second group of devices.