Improving Deep Learning for Defect Prediction (using the GHOST Hyperparameter Optimizer)
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There has been much recent interest in the application of deep learning neural networks in software engineering. Some researchers are worried that deep learning is being applied with insufficient critical ssessment. Hence, for one well-studied software analytics task (defect prediction), this paper compares deep learning versus prior-state-of-the-art results. Deep learning will outperform those prior results, but only after adjusting its hyperparameters using GHOST (Goal-oriented Hyperparameter Optimization for Scalable Training). For defect prediction, GHOST terminates in just a few minutes and scales to larger data sets; i.e. it is practical to tune deep learning tuning for defect prediction. Hence this paper recommends deep learning for defect prediction, but only adjusting its goal predicates and tuning its hyperparameters (using some hyperparameter optimization tool, like GHOST)
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