  Text mining methods are used for a wide range of Software Engineering (SE)
tasks. The biggest challenge of text mining is high dimensional data, i.e., a
corpus of documents can contain $10^4$ to $10^6$ unique words. To address this
complexity, some very convoluted text mining methods have been applied. Is that
complexity necessary? Are there simpler ways to quickly generate models that
perform as well as the more convoluted methods and also be human-readable?
  To answer these questions, we explore a combination of LDA (Latent Dirichlet
Allocation) and FFTs (Fast and Frugal Trees) to classify NASA software bug
reports from six different projects. Designed using principles from
psychological science, FFTs return very small models that are
human-comprehensible. When compared to the commonly used text mining method and
a recent state-of-the-art-system (search-based SE method that automatically
tune the control parameters of LDA), these FFT models are very small (a binary
tree of depth $d = 4$ that references only 4 topics) and hence easy to
understand. They were also faster to generate and produced similar or better
severity predictions.
  Hence we can conclude that, at least for datasets explored here, convoluted
text mining models can be deprecated in favor of simpler method such as
LDA+FFTs. At the very least, we recommend LDA+FFTs (a) when humans need to
read, understand, and audit a model or (b) as an initial baseline method for
the SE researchers exploring text artifacts from software projects.
