# Applications of Psychological Science for Actionable Analytics

## TL;DR

This paper demonstrates that Fast-and-Frugal Trees (FFTs), simple and psychologically-inspired models, outperform complex defect prediction algorithms in software analytics, especially with operational data, highlighting the value of psychological insights and simple baselines.

## Contribution

The study introduces FFTs into software analytics, showing their effectiveness and advocating for psychological science-inspired models and simple baselines in the field.

## Key findings

- FFTs are very succinct, with models of 5 lines or less.
- FFTs outperform state-of-the-art defect prediction algorithms.
- FFTs perform significantly better when trained on operational attributes.

## Abstract

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.