  How to make software analytics simpler and faster? One method is to match the
complexity of analysis to the intrinsic complexity of the data being explored.
For example, hyperparameter optimizers find the control settings for data
miners that improve for improving the predictions generated via software
analytics. Sometimes, very fast hyperparameter optimization can be achieved by
just DODGE-ing away from things tried before. But when is it wise to use DODGE
and when must we use more complex (and much slower) optimizers? To answer this,
we applied hyperparameter optimization to 120 SE data sets that explored bad
smell detection, predicting Github ssue close time, bug report analysis, defect
prediction, and dozens of other non-SE problems. We find that DODGE works best
for data sets with low "intrinsic dimensionality" (D = 3) and very poorly for
higher-dimensional data (D over 8). Nearly all the SE data seen here was
intrinsically low-dimensional, indicating that DODGE is applicable for many SE
analytics tasks.
