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Though many deep learning-based models have made great progress in vulnerability detection, we have no good understanding of these models, which limits the further advancement of model capability, understanding of the mechanism of model detection, and efficiency and safety of practical application of models.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
GNNExplainer: Generating Explanations for Graph Neural Networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019 · 1903
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 · 1958
Earlier work this paper cites.
Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability , Vol. 1. Oakland, CA, USA, 281–297
James MacQueen et al · 1967
Earlier work this paper cites.
Seven pernicious kingdoms: A taxonomy of software security errors
Katrina Tsipenyuk, Brian Chess, and Gary McGraw. 2005 · 2005
Earlier work this paper cites.
Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Modeling and discovering vulnerabilities with code property graphs. In 2014 IEEE Symposium on Security and Privacy . IEEE, 590–604
Fabian Yamaguchi, Nico Golde, Daniel Arp, and Konrad Rieck. 2014 · 2014
Earlier work this paper cites.
Automatic feature learning for vulnerability prediction
Hoa Khanh Dam, Truyen Tran, Trang Pham, Shien Wee Ng, John Grundy, and Aditya Ghose. 2017 · 2017
Earlier work this paper cites.
Large-scale identification of malicious singleton files. In Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy . 227–238
Bo Li, Kevin Roundy, Chris Gates, and Yevgeniy Vorobeychik. 2017 · 2017
Earlier work this paper cites.
POSTER: Vulnerability discovery with function representation learning from unlabeled projects. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . 2539–2541
Guanjun Lin, Jun Zhang, Wei Luo, Lei Pan, and Yang Xiang. 2017 · 2017
Earlier work this paper cites.
Droidsieve: Fast and accurate classification of obfuscated android malware. In Proceedings of the seventh ACM on conference on data and application security and privacy . 309–320
Guillermo Suarez-Tangil, Santanu Kumar Dash, Mansour Ahmadi, Johannes Kinder, Giorgio Giacinto, and Lorenzo Cavallaro. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Software assurance reference dataset (SARD)
2018 · 2018
Earlier work this paper cites.
Vuldeepecker: A deep learning-based system for vulnerability detection. In Proceedings of the 25th Annual Network and Distributed System Security Symposium
Zhen Li, Deqing Zou, Shouhuai Xu, Xinyu Ou, Hai Jin, Sujuan Wang, Zhijun Deng, and Yuyi Zhong. 2018 · 2018
Earlier work this paper cites.
Automated vulnerability detection in source code using deep representation learning. In 2018 17th IEEE international conference on machine learning and applications (ICMLA) . IEEE, 757–762
Rebecca Russell, Louis Kim, Lei Hamilton, Tomo Lazovich, Jacob Harer, Onur Ozdemir, Paul Ellingwood, and Marc McConley. 2018 · 2018
Earlier work this paper cites.
A performance evaluation of deep-learnt features for software vulnerability detection
Xinbo Ban, Shigang Liu, Chao Chen, and Caslon Chua. 2019 · 2019
Earlier work this paper cites.
VulSniper: Focus Your Attention to Shoot Fine-Grained Vulnerabilities.. In IJCAI . 4665–4671
Xu Duan, Jingzheng Wu, Shouling Ji, Zhiqing Rui, Tianyue Luo, Mutian Yang, and Yanjun Wu. 2019 · 2019
Earlier work this paper cites.
Digital investigation of pdf files: Unveiling traces of embedded malware
Davide Maiorca and Battista Biggio. 2019 · 2019
Earlier work this paper cites.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Earlier work this paper cites.
Sofia Serrano and Noah A Smith. 2019 · 2019
Earlier work this paper cites.
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2019 · 2019
Earlier work this paper cites.
Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks. In In Proceedings of the 33rd International Conference on Neural Information Processing Systems . 10197–10207
Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu. 2019 · 2019
Cited alongside, same era.
A C/C++ code vulnerability dataset with code changes and CVE summaries. In Proceedings of the 17th International Conference on Mining Software Repositories . 508–512
Jiahao Fan, Yi Li, Shaohua Wang, and Tien N Nguyen. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020 . Association for Computational Linguistics, Online, 1536–1547
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020a · 2020
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 11048–11064
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Later among the works it cites.
The Best of Both Worlds: Integrating Semantic Features with Expert Features for Defect Prediction and Localization. In Proceedings of the 2022 30th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . ACM, 672–683
Chao Ni, Wei Wang, Kaiwen Yang, Xin Xia, Kui Liu, and David Lo. 2022a · 2022
Later among the works it cites.
Defect Identification, Categorization, and Repair: Better Together
Chao Ni, Kaiwen Yang, Xin Xia, David Lo, Xiang Chen, and Xiaohu Yang. 2022b · 2022
Later among the works it cites.
ChatGPT: Optimizing Language Models for Dialogue. (2022)
OpenAI. 2022 · 2022
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Cited alongside, same era.
A comparative study of neural network techniques for automatic software vulnerability detection. In 2020 International symposium on theoretical aspects of software engineering (TASE) . IEEE, 1–8
Gaigai Tang, Lianxiao Meng, Huiqiang Wang, Shuangyin Ren, Qiang Wang, Lin Yang, and Weipeng Cao. 2020 · 2020
Cited alongside, same era.
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020 · 2020
Cited alongside, same era.
Deep learning based vulnerability detection: Are we there yet
Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, and Baishakhi Ray. 2021 · 2021
Cited alongside, same era.
Deepwukong: Statically detecting software vulnerabilities using deep graph neural network
Xiao Cheng, Haoyu Wang, Jiayi Hua, Guoai Xu, and Yulei Sui. 2021 · 2021
Cited alongside, same era.
Vuldeelocator: a deep learning-based fine-grained vulnerability detector
Zhen Li, Deqing Zou, Shouhuai Xu, Zhaoxuan Chen, Yawei Zhu, and Hai Jin. 2021b · 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. 2021c · 2021
Cited alongside, same era.
Deep neural-based vulnerability discovery demystified: data, model and performance
Guanjun Lin, Wei Xiao, Leo Yu Zhang, Shang Gao, Yonghang Tai, and Jun Zhang. 2021 · 2021
Cited alongside, same era.
What Makes Good In-Context Examples for GPT- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
HGVul: A code vulnerability detection method based on heterogeneous source-level intermediate representation
Zihua Song, Junfeng Wang, Shengli Liu, Zhiyang Fang, Kaiyuan Yang, et al · 2022
Later among the works it cites.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
Later among the works it cites.
Diet code is healthy: Simplifying programs for pre-trained models of code. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1073–1084
Zhaowei Zhang, Hongyu Zhang, Beijun Shen, and Xiaodong Gu. 2022 · 2022
Later among the works it cites.
Data quality for software vulnerability datasets. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 121–133
Roland Croft, M Ali Babar, and M Mehdi Kholoosi. 2023 · 2023
Later among the works it cites.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
Later among the works it cites.
Distinguishing Look-Alike Innocent and Vulnerable Code by Subtle Semantic Representation Learning and Explanation. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1611–1622
Chao Ni, Xin Yin, Kaiwen Yang, Dehai Zhao, Zhenchang Xing, and Xin Xia. 2023 · 2023
Later among the works it cites.
An empirical study of deep learning models for vulnerability detection. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2237–2248
Benjamin Steenhoek, Md Mahbubur Rahman, Richard Jiles, and Wei Le. 2023 · 2023
Later among the works it cites.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2023 · 2023
Later among the works it cites.
DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2249–2261
Wenbo Wang, Tien N Nguyen, Shaohua Wang, Yi Li, Jiyuan Zhang, and Aashish Yadavally. 2023 · 2023
Later among the works it cites.
Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning. In 45th IEEE/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, May 14-20, 2023 . IEEE, 2275–2286
Xin-Cheng Wen, Yupan Chen, Cuiyun Gao, Hongyu Zhang, Jie M. Zhang, and Qing Liao. 2023 · 2023
Later among the works it cites.
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