According to
psychological scientists, humans understand models that most match their own internal models,
which they characterize as lists of "heuristic"s (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 in that their models are very succinct (5 lines or less describing a binary decision tree) while also
outperforming result from very recent, top-level,
conference papers.
Also, when we restrict training data to operational attributes (i.e., those attributes that are frequently changed by developers), the performance of FFTs are not effected
(while the performance of other learners can vary wildly).

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.