Predicting User Experience on Laptops from Hardware Specifications
Saswat Padhi Sunil K. Bhasin Udaya K. Ammu Alex Bergman Allan Knies
Estimating the overall user experience (UX) on a device is a common challenge
faced by manufacturers. Today, device makers primarily rely on microbenchmark
scores, such as Geekbench, that stress test specific hardware components, such
as CPU or RAM, but do not satisfactorily capture consumer workloads. System
designers often rely on domain-specific heuristics and extensive testing of
prototypes to reach a desired UX goal, and yet there is often a mismatch
between the manufacturers' performance claims and the consumers' experience.
  We present our initial results on predicting real-life experience on laptops
from their hardware specifications. We target web applications that run on
Chromebooks (ChromeOS laptops) for a simple and fair aggregation of experience
across applications and workloads. On 54 laptops, we track 9 UX metrics on
common end-user workloads: web browsing, video playback and audio/video calls.
We focus on a subset of high-level metrics exposed by the Chrome browser, that
are part of the Web Vitals initiative for judging the UX on web applications.
  With a dataset of 100K UX data points, we train gradient boosted regression
trees that predict the metric values from device specifications. Across our 9
metrics, we note a mean $R^2$ score (goodness-of-fit on our dataset) of 97.8%
and a mean MAAPE (percentage error in prediction on unseen data) of 10.1%.