Enhancing Security Patch Identification by Capturing Structures in Commits

07/19/2022
by   Bozhi Wu, et al.
0

With the rapid increasing number of open source software (OSS), the majority of the software vulnerabilities in the open source components are fixed silently, which leads to the deployed software that integrated them being unable to get a timely update. Hence, it is critical to design a security patch identification system to ensure the security of the utilized software. However, most of the existing works for security patch identification just consider the changed code and the commit message of a commit as a flat sequence of tokens with simple neural networks to learn its semantics, while the structure information is ignored. To address these limitations, in this paper, we propose our well-designed approach E-SPI, which extracts the structure information hidden in a commit for effective identification. Specifically, it consists of the code change encoder to extract the syntactic of the changed code with the BiLSTM to learn the code representation and the message encoder to construct the dependency graph for the commit message with the graph neural network (GNN) to learn the message representation. We further enhance the code change encoder by embedding contextual information related to the changed code. To demonstrate the effectiveness of our approach, we conduct the extensive experiments against six state-of-the-art approaches on the existing dataset and from the real deployment environment. The experimental results confirm that our approach can significantly outperform current state-of-the-art baselines.

READ FULL TEXT
research
08/07/2021

PatchRNN: A Deep Learning-Based System for Security Patch Identification

With the increasing usage of open-source software (OSS) components, vuln...
research
09/15/2023

Silent Vulnerability-fixing Commit Identification Based on Graph Neural Networks

The growing dependence of software projects on external libraries has ge...
research
10/14/2021

ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection

Identifying vulnerabilities in the source code is essential to protect t...
research
02/08/2023

CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back

Representing code changes as numeric feature vectors, i.e., code change ...
research
08/29/2023

Multilevel Semantic Embedding of Software Patches: A Fine-to-Coarse Grained Approach Towards Security Patch Detection

The growth of open-source software has increased the risk of hidden vuln...
research
11/23/2022

DeepVulSeeker: A Novel Vulnerability Identification Framework via Code Graph Structure and Pre-training Mechanism

Software vulnerabilities can pose severe harms to a computing system. Th...
research
08/31/2023

Learning to Represent Patches

Patch representation is crucial in automating various software engineeri...

Please sign up or login with your details

Forgot password? Click here to reset