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With the rapid increasing number of open source software (OSS), the majorityof the software vulnerabilities in the open source components are fixedsilently, which leads to the deployed software that integrated them beingunable to get a timely update. Hence, it is critical to design a security patchidentification system to ensure the security of the utilized software. However,most of the existing works for security patch identification just consider thechanged code and the commit message of a commit as a flat sequence of tokenswith simple neural networks to learn its semantics, while the structureinformation is ignored. To address these limitations, in this paper, we proposeour well-designed approach E-SPI, which extracts the structure informationhidden in a commit for effective identification. Specifically, it consists ofthe code change encoder to extract the syntactic of the changed code with theBiLSTM to learn the code representation and the message encoder to constructthe dependency graph for the commit message with the graph neural network (GNN)to learn the message representation. We further enhance the code change encoderby embedding contextual information related to the changed code. To demonstratethe effectiveness of our approach, we conduct the extensive experiments againstsix state-of-the-art approaches on the existing dataset and from the realdeployment environment. The experimental results confirm that our approach cansignificantly outperform current state-of-the-art baselines.

Enhancing Security Patch Identification by Capturing Structures in Commits