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Deep learning based software defect prediction
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DOI：10.1016/j.neucom.2019.11.067.png)
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Abstract
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Software systems have become larger and more complex than ever. Such characteristics make it very challengeable to prevent software defects. Therefore, automatically predicting the number of defects in software modules is necessary and may help developers efficiently to allocate limited resources. Various approaches have been proposed to identify and fix such defects at minimal cost. However, the performance of these approaches require significant improvement. Therefore, in this paper, we propose a novel approach that leverages deep learning techniques to predict the number of defects in software systems. First, we preprocess a publicly available dataset, including log transformation and data normalization. Second, we perform data modeling to prepare the data input for the deep learning model. Third, we pass the modeled data to a specially designed deep neural network-based model to predict the number of defects. We also evaluate the proposed approach on two well-known datasets. The evaluation results illustrate that the proposed approach is accurate and can improve upon the state-of-the-art approaches. On average, the proposed method significantly reduces the mean square error by more than 14% and increases the squared correlation coefficient by more than 8%. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Software defect prediction
Deep learning
Software quality
Software metrics
Robustness evaluation
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