Title: Perceived Performance of Webpages In the Wild: Insights from Large-scale Crowdsourcing of Above-the-Fold QoE,
Abstract: Clearly, no one likes webpages with poor quality of experience (QoE). Being
perceived as slow or fast is a key element in the overall perceived QoE of web
applications. While extensive effort has been put into optimizing web
applications (both in industry and academia), not a lot of work exists in
characterizing what aspects of webpage loading process truly influence human
end-user's perception of the "Speed" of a page. In this paper we present
"SpeedPerception", a large-scale web performance crowdsourcing framework
focused on understanding the perceived loading performance of above-the-fold
(ATF) webpage content. Our end goal is to create free open-source benchmarking
datasets to advance the systematic analysis of how humans perceive webpage
loading process. In Phase-1 of our "SpeedPerception" study using Internet
Retailer Top 500 (IR 500) websites
(this https URL), we found that commonly used
navigation metrics such as "onLoad" and "Time To First Byte (TTFB)" fail (less
than 60% match) to represent majority human perception when comparing the speed
of two webpages. We present a simple 3-variable-based machine learning model
that explains the majority end-user choices better (with $87 \pm 2\%$
accuracy). In addition, our results suggest that the time needed by end-users
to evaluate relative perceived speed of webpage is far less than the time of
its "visualComplete" event.