Fetching the paper…
Reading the bibliography…
Code Language Models (codeLMs) and Graph Neural Networks (GNNs) are widely used in code vulnerability detection.
Modeling and discovering vulnerabilities with code property graphs
Fabian Yamaguchi, Nico Golde, Daniel Arp, and Konrad Rieck · 2014
Earlier work this paper cites.
Impact assessment for vulnerabilities in open-source software libraries
Henrik Plate, Serena Elisa Ponta, and Antonino Sabetta · 2015
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Earlier work this paper cites.
Automated vulnerability detection in source code using deep representation learning
Rebecca Russell, Louis Kim, Lei Hamilton, Tomo Lazovich, Jacob Harer, Onur Ozdemir, Paul Ellingwood, and Marc McConley · 2018
Earlier work this paper cites.
Vuldeepecker: A deep learning-based system for vulnerability detection
Zhen Li, Deqing Zou, Shouhuai Xu, Xinyu Ou, Hai Jin, Sujuan Wang, Zhijun Deng, and Yuyi Zhong · 2018
Earlier work this paper cites.
Automated software vulnerability detection with machine learning
Jacob A Harer, Louis Y Kim, Rebecca L Russell, Onur Ozdemir, Leonard R Kosta, Akshay Rangamani, Lei H Hamilton, Gabriel I Centeno, Jonathan R Key, Paul M Ellingwood, et al · 2018
Earlier work this paper cites.
Taku Kudo and John Richardson · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Software vulnerability discovery via learning multi-domain knowledge bases
Guanjun Lin, Jun Zhang, Wei Luo, Lei Pan, Olivier De Vel, Paul Montague, and Yang Xiang · 2019
Earlier work this paper cites.
Jsac: A novel framework to detect malicious javascript via cnns over ast and cfg
Hongliang Liang, Yuxing Yang, Lu Sun, and Lin Jiang · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks
Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu · 2019
Earlier work this paper cites.
Graph convolutional networks for text classification
Liang Yao, Chengsheng Mao, and Yuan Luo · 2019
Earlier work this paper cites.
Improving text classification with weighted word embeddings via a multi-channel textcnn model
Bao Guo, Chunxia Zhang, Junmin Liu, and Xiaoyi Ma · 2019
Earlier work this paper cites.
Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan · 2019
Earlier work this paper cites.
Software vulnerability detection using deep neural networks: a survey
Guanjun Lin, Sheng Wen, Qing-Long Han, Jun Zhang, and Yang Xiang · 2020
Earlier work this paper cites.
Combining graph-based learning with automated data collection for code vulnerability detection
Huanting Wang, Guixin Ye, Zhanyong Tang, Shin Hwei Tan, Songfang Huang, Dingyi Fang, Yansong Feng, Lizhong Bian, and Zheng Wang · 2020
Earlier work this paper cites.
Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
Earlier work this paper cites.
Graphcodebert: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al · 2020
Cited alongside, same era.
Learning to map source code to software vulnerability using code-as-a-graph
Sahil Suneja, Yunhui Zheng, Yufan Zhuang, Jim Laredo, and Alessandro Morari · 2020
Cited alongside, same era.
Graph neural network-based vulnerability predication
Qi Feng, Chendong Feng, and Weijiang Hong · 2020
Cited alongside, same era.
Distilling knowledge from graph convolutional networks
Yiding Yang, Jiayan Qiu, Mingli Song, Dacheng Tao, and Xinchao Wang · 2020
Cited alongside, same era.
Learning and evaluating contextual embedding of source code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi · 2020
Cited alongside, same era.
Path-sensitive code embedding via contrastive learning for software vulnerability detection
Xiao Cheng, Guanqin Zhang, Haoyu Wang, and Yulei Sui · 2022
Later among the works it cites.
Linevd: statement-level vulnerability detection using graph neural networks
David Hin, Andrey Kan, Huaming Chen, and M Ali Babar · 2022
Later among the works it cites.
Alignahead: online cross-layer knowledge extraction on graph neural networks
Jiongyu Guo, Defang Chen, and Can Wang · 2022
Later among the works it cites.
Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2022
Later among the works it cites.
How effective is byte pair encoding for out-of-vocabulary words in neural machine translation?
Ali Araabi, Christof Monz, and Vlad Niculae · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
μ \mu vuldeepecker: A deep learning-based system for multiclass vulnerability detection
Deqing Zou, Sujuan Wang, Shouhuai Xu, Zhen Li, and Hai Jin · 2021
Cited alongside, same era.
Sysevr: A framework for using deep learning to detect software vulnerabilities
Zhen Li, Deqing Zou, Shouhuai Xu, Hai Jin, Yawei Zhu, and Zhaoxuan Chen · 2021
Cited alongside, same era.
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi · 2021
Cited alongside, same era.
Cotext: Multi-task learning with code-text transformer
Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, and Yanfang Ye · 2021
Cited alongside, same era.
Vulnerability detection with fine-grained interpretations
Yi Li, Shaohua Wang, and Tien N Nguyen · 2021
Cited alongside, same era.
Deep learning based vulnerability detection: Are we there yet?
Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, and Baishakhi Ray · 2021
Cited alongside, same era.
Vu1spg: Vulnerability detection based on slice property graph representation learning
Weining Zheng, Yuan Jiang, and Xiaohong Su · 2021
Cited alongside, same era.
Later among the works it cites.
Vulberta: Simplified source code pre-training for vulnerability detection
Hazim Hanif and Sergio Maffeis · 2022
Later among the works it cites.
Natgen: generative pre-training by “naturalizing” source code
Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding, Premkumar T Devanbu, and Baishakhi Ray · 2022
Later among the works it cites.
A systematic evaluation of large language models of code
Frank F Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn · 2022
Later among the works it cites.
Understanding and tackling label errors in deep learning-based vulnerability detection (experience paper)
Xu Nie, Ningke Li, Kailong Wang, Shangguang Wang, Xiapu Luo, and Haoyu Wang · 2023
Later among the works it cites.
Continual graph learning: A survey
Qiao Yuan, Sheng-Uei Guan, Pin Ni, Tianlun Luo, Ka Lok Man, Prudence Wong, and Victor Chang · 2023
Later among the works it cites.
Interpreters for gnn-based vulnerability detection: Are we there yet?
Yutao Hu, Suyuan Wang, Wenke Li, Junru Peng, Yueming Wu, Deqing Zou, and Hai Jin · 2023
Later among the works it cites.
The bug hunter’s workbench, 2023
Anon · 2023
Later among the works it cites.
Online cross-layer knowledge distillation on graph neural networks with deep supervision
Jiongyu Guo, Defang Chen, and Can Wang · 2023
Later among the works it cites.
Diversevul: A new vulnerable source code dataset for deep learning based vulnerability detection
Yizheng Chen, Zhoujie Ding, Lamya Alowain, Xinyun Chen, and David Wagner · 2023
Later among the works it cites.
Vulmae: Graph masked autoencoders for vulnerability detection from source and binary codes
Mahmoud Zamani, Saquib Irtiza, Latifur Khan, and Kevin W Hamlen · 2023
Later among the works it cites.
Csgvd: A deep learning approach combining sequence and graph embedding for source code vulnerability detection
Wei Tang, Mingwei Tang, Minchao Ban, Ziguo Zhao, and Mingjun Feng · 2023
Later among the works it cites.
Towards efficient fine-tuning of pre-trained code models: An experimental study and beyond
Ensheng Shi, Yanlin Wang, Hongyu Zhang, Lun Du, Shi Han, Dongmei Zhang, and Hongbin Sun · 2023
Later among the works it cites.
Knowledge graph and deep learning-based text-to-graphql model for intelligent medical consultation chatbot
Pin Ni, Ramin Okhrati, Steven Guan, and Victor Chang · 2024
Closest in time.
Cfexplainer: Explainable just-in-time defect prediction based on counterfactuals
Fengyu Yang, Guangdong Zeng, Fa Zhong, Peng Xiao, Wei Zheng, and Fuxing Qiu · 2024
Closest in time.