  One truism of deep learning is that the automatic feature engineering (seen
in the first layers of those networks) excuses data scientists from performing
tedious manual feature engineering prior to running DL. For the specific case
of deep learning for defect prediction, we show that that truism is false.
Specifically, when we preprocess data with a novel oversampling technique
called fuzzy sampling, as part of a larger pipeline called GHOST (Goal-oriented
Hyper-parameter Optimization for Scalable Training), then we can do
significantly better than the prior DL state of the art in 14/20 defect data
sets. Our approach yields state-of-the-art results significantly faster deep
learners. These results present a cogent case for the use of oversampling prior
to applying deep learning on software defect prediction datasets.
