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

On the Value of Oversampling for Deep Learning in Software Defect Prediction