[Research] Software Defect Prediction via Multi-Channel Convolutional Neural Network
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Chen Lang presented our paper in The 21st IEEE International Conference on Quality Software (QRS 2021).
Authors: Chen Lang, Jidong Li, Takashi Kobayashi
Title: Software Defect Prediction via Multi-Channel
Book Title: Proc. The 21st IEEE International Conference on Quality Software (QRS 2021), Dec. 6-10, 2021
With the growing complexity of modern software, the expense of improving software reliability becomes significant. To reduce costs, Software Defect Prediction (SDP) was proposed to detect hidden bugs a few decades ago. Recent studies demon-strate the superiority of the deep learning-based approach in SDP, such as Convolutional Neural Network (CNN). However, the noise in defect data can still deteriorate their performance. To enhance the performance of the state-of-the-art CNN-based method, we propose an improved SDP framework named Defect Prediction via Multi-Channel Convolutional Neural Network (DP-MC-CNN). The novelty is that DP-MC-CNN extracts sub-trees from the Abstract Syntax Tree, generates multiple paths, and feeds them to a multi-channel CNN, which weakens the noise effect. In evaluation, we assess DP-MC-CNN on seven projects by average F1 score, and it outperforms the state-of-the-art CNN-based method by 3.93%. Furthermore, we validate the effectiveness of the multi-channel approach and reveal the influences of each sub-tree component.