  Although there are many automated software testing suites, they usually focus
on unit, system, and interface testing. However, especially software updates
such as new security features have the potential to diminish user experience.
In this paper, we propose a novel automated user experience testing methodology
that learns how code changes impact the time unit and system tests take, and
extrapolate user experience changes based on this information. Such a tool can
be integrated into existing continuous integration pipelines, and it provides
software teams immediate user experience feedback. We construct a feature set
from lexical, layout, and syntactic characteristics of the code, and using
Abstract Syntax Tree-Based Embeddings, we can calculate the approximate
semantic distance to feed into a machine learning algorithm. In our
experiments, we use several regression methods to estimate the time impact of
software updates. Our open-source tool achieved 3.7% mean absolute error rate
with a random forest regressor.
