Abstract：Existing deep learning based source code vulnerability detection methods use training and test data sets that are mostly derived from test source codes for academic research only which do not provide sufficient support for training of deep learning models. This paper presents a data generation and annotation method for source code defect detection. This method extracts the source code control flow relationships and uses trained deep learning models and commercial tools to complete the slice data annotation of the source code. The public data sets SARD, NVD and the open-source code Ffmpeg are utilized to verify the system performance. The results show that this method can generate a source code defect dataset for deep learning to support deep learning-based source code vulnerability detection methods.
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