  Static code warning tools often generate warnings that programmers ignore.
Such tools can be made more useful via data mining algorithms that select the
"actionable" warnings; i.e. the warnings that are usually not ignored.
  In this paper, we look for actionable warnings within a sample of 5,675
actionable warnings seen in 31,058 static code warnings from FindBugs. We find
that data mining algorithms can find actionable warnings with remarkable ease.
Specifically, a range of data mining methods (deep learners, random forests,
decision tree learners, and support vector machines) all achieved very good
results (recalls and AUC (TRN, TPR) measures usually over 95% and false alarms
usually under 5%).
  Given that all these learners succeeded so easily, it is appropriate to ask
if there is something about this task that is inherently easy. We report that
while our data sets have up to 58 raw features, those features can be
approximated by less than two underlying dimensions. For such intrinsically
simple data, many different kinds of learners can generate useful models with
similar performance.
  Based on the above, we conclude that learning to recognize actionable static
code warnings is easy, using a wide range of learning algorithms, since the
underlying data is intrinsically simple. If we had to pick one particular
learner for this task, we would suggest linear SVMs (since, at least in our
sample, that learner ran relatively quickly and achieved the best median
performance) and we would not recommend deep learning (since this data is
intrinsically very simple).
