Abstract：Deep learning-based source code defect detection looks at the source code as text data. The defect detection then uses a one-dimensional convolutional network to learn the single spatial characteristics of the code or uses the sequential characteristics of LSTM and BiLSTM which do not take various features of the source code into account. This article uses the multi-channel learning strategy of convolutional neural networks for image classification to identify multi-class source code defects by deep convolutional neural networks. First, a word embedding algorithm such as word2vec or fasttext is used to construct the fusion features with the deep convolutional neural network then used to identify the defect patterns contained in the source code defect data set to form a source code defect classifier. The classifier is then used to recognize defect codes and their corresponding CWE type. The method was evaluated on the SARD dataset and open source software. The results show that this method is superior to existing methods with a model evaluation parameter accuracy of 95.3%, a recall rate of 84.7%, and F1 of 89.7%.
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