Fetching the paper…
Reading the bibliography…
Deep learning vulnerability detection has shown promising results in recent years.
Causality: Models, reasoning, and inference
Judea Pearl. 2000 · 2000
Earlier work this paper cites.
Transportability of causal and statistical relations: A formal approach. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 25. 247–254
Judea Pearl and Elias Bareinboim. 2011 · 2011
Earlier work this paper cites.
Cybercrime To Cost The World $10.5 Trillion Annually By 2025, howpublished =https://cybersecurityventures.com/hackerpocalypse-cybercrime-report-2016/
[n. d.] · 2016
Earlier work this paper cites.
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. In Advances in Neural Information Processing Systems , Vol. 32. 10197–10207
Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu. 2019 · 2019
Earlier work this paper cites.
A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries. In Proceedings of the 17th International Conference on Mining Software Repositories (Seoul, Republic of Korea) (MSR ’20) . Association for Computing Machinery, New York, NY, USA, 508–512
Jiahao Fan, Yi Li, Shaohua Wang, and Tien N. Nguyen. 2020 · 2020
Earlier work this paper cites.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020 . 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. 2020 · 2020
Earlier work this paper cites.
Unified Pre-training for Program Understanding and Generation. In 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Earlier work this paper cites.
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations (SIGIR ’21) . Association for Computing Machinery, New York, NY, USA, 511–521
Nghi D. Q. Bui, Yijun Yu, and Lingxiao Jiang. 2021 · 2021
Earlier work this paper cites.
Deep Learning based Vulnerability Detection: Are We There Yet
Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, and Baishakhi Ray. 2021 · 2021
Earlier work this paper cites.
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021 · 2021
Cited alongside, same era.
GraphCodeBERT: Pre-training Code Representations with Data Flow. In International Conference on Learning Representations
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Jian Yin, Daxin Jiang, et al · 2021
Cited alongside, same era.
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al · 2021
Cited alongside, same era.
LineVD: Statement-Level Vulnerability Detection Using Graph Neural Networks. In Proceedings of the 19th International Conference on Mining Software Repositories (Pittsburgh PA) (MSR ’22) . 596–607
David Hin, Andrey Kan, Huaming Chen, and M. Ali Babar. 2022 · 2022
Later among the works it cites.
Unicorn: Reasoning about Configurable System Performance through the Lens of Causality. In Proceedings of the Seventeenth European Conference on Computer Systems (Rennes, France) (EuroSys ’22) . Association for Computing Machinery, New York, NY, USA, 199–217
Md Shahriar Iqbal, Rahul Krishna, Mohammad Ali Javidian, Baishakhi Ray, and Pooyan Jamshidi. 2022 · 2022
Later among the works it cites.
Causal Transportability for Visual Recognition. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE Computer Society, Los Alamitos, CA, USA, 7511–7521
C. Mao, K. Xia, J. Wang, H. Wang, J. Yang, E. Bareinboim, and C. Vondrick. 2022 · 2022
Later among the works it cites.
ReCode: Robustness Evaluation of Code Generation Models
Shiqi Wang, Zheng Li, Haifeng Qian, Chenghao Yang, Zijian Wang, Mingyue Shang, Varun Kumar, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, and Bing Xiang. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022 · 2022
Cited alongside, same era.
MVD: Memory-Related Vulnerability Detection Based on Flow-Sensitive Graph Neural Networks. In Proceedings of the 44th International Conference on Software Engineering (Pittsburgh PA) (ICSE ’22) . 1456–1468
Sicong Cao, Xiaobing Sun, Lili Bo, Rongxin Wu, Bin Li, and Chuanqi Tao. 2022 · 2022
Cited alongside, same era.
Counterfactual Explanations for Models of Code. In Proceedings of the 44th International Conference on Software Engineering: Software Engineering in Practice (Pittsburgh, Pennsylvania) (ICSE-SEIP ’22) . Association for Computing Machinery, New York, NY, USA, 125–134
Jürgen Cito, Isil Dillig, Vijayaraghavan Murali, and Satish Chandra. 2022 · 2022
Cited alongside, same era.
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 6300–6312
Yangruibo Ding, Luca Buratti, Saurabh Pujar, Alessandro Morari, Baishakhi Ray, and Saikat Chakraborty. 2022 · 2022
Cited alongside, same era.
LineVul: A Transformer-based Line-Level Vulnerability Prediction. In 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR) . 608–620
Michael Fu and Chakkrit Tantithamthavorn. 2022 · 2022
Cited alongside, same era.
UniXcoder: Unified Cross-Modal Pre-training for Code Representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
Cited alongside, same era.
Microsoft Exchange Flaw: Attacks Surge After Code Published, howpublished =https://www.bankinfosecurity.com/ms-exchange-flaw-causes-spike-in-trdownloader-gen-trojans-a-16236
[n. d.]
Cited in the paper.
Later among the works it cites.
Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems. In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (Virtual, South Korea) (ISSTA 2022) . Association for Computing Machinery, New York, NY, USA, 493–505
Ziyuan Zhong, Zhisheng Hu, Shengjian Guo, Xinyang Zhang, Zhenyu Zhong, and Baishakhi Ray. 2022 · 2022
Later among the works it cites.
TRACED: Execution-aware Pre-training for Source Code
Yangruibo Ding, Ben Steenhoek, Kexin Pei, Gail Kaiser, Wei Le, and Baishakhi Ray. 2023 · 2023
Closest in time.
An Empirical Study of Deep Learning Models for Vulnerability Detection
Benjamin Steenhoek, Md Mahbubur Rahman, Richard Jiles, and Wei Le. 2023 · 2023
Closest in time.
DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . 2249–2261
Wenbo Wang, Tien N. Nguyen, Shaohua Wang, Yi Li, Jiyuan Zhang, and Aashish Yadavally. 2023 · 2023
Closest in time.