Applications of Psychological Science for Actionable Analytics
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
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,
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.
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Model Transformation Languages under a Magnifying Glass: A Controlled Experiment with Xtend, ATL, and QVT
Regina HebigChalmers University of Technology & University of Gothenburg, Christoph SeidlTechnische Universität Braunschweig, Thorsten BergerChalmers University of Technology, Sweden / University of Gothenburg, Sweden, John Kook PedersenIT University of Copenhagen, Denmark, Andrzej WąsowskiIT University of Copenhagen, Denmark