  Actionable analytics are those that humans can understand, and
operationalize. What kind of data mining models generate such actionable
analytics? According to psychological scientists, humans understand models that
most match their own internal models, which they characterize as lists of
"heuristic" (i.e., lists of very succinct rules). One such heuristic rule
generator is the Fast-and-Frugal Trees (FFT) preferred by psychological
scientists. Despite their successful use in many applied domains, FFTs have not
been applied in software analytics. Accordingly, this paper assesses FFTs for
software analytics.
  We find that FFTs are remarkably effective. Their models are very succinct (5
lines or less describing a binary decision tree). These succinct models
outperform state-of-the-art defect prediction algorithms defined by Ghortra et
al. at ICSE'15. Also, when we restrict training data to operational attributes
(i.e., those attributes that are frequently changed by developers), FFTs
perform much better than standard learners.
  Our conclusions are two-fold. Firstly, there is much that software analytics
community could learn from psychological science. Secondly, proponents of
complex methods should always baseline those methods against simpler
alternatives. For example, FFTs could be used as a standard baseline learner
against which other software analytics tools are compared.
