  Yes. SE data can have "smoother" boundaries between classes (compared to
traditional AI data sets). To be more precise, the magnitude of the second
derivative of the loss function found in SE data is typically much smaller. A
new hyper-parameter optimizer, called SMOOTHIE, can exploit this idiosyncrasy
of SE data. We compare SMOOTHIE and a state-of-the-art AI hyper-parameter
optimizer on three tasks: (a) GitHub issue lifetime prediction (b) detecting
static code warnings false alarm; (c) defect prediction. For completeness, we
also show experiments on some standard AI datasets. SMOOTHIE runs faster and
predicts better on the SE data--but ties on non-SE data with the AI tool. Hence
we conclude that SE data can be different to other kinds of data; and those
differences mean that we should use different kinds of algorithms for our data.
To support open science and other researchers working in this area, all our
scripts and datasets are available on-line at
https://github.com/yrahul3910/smoothness-hpo/.
