Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) are transforming software engineering tasks, including code vulnerability detection-a critical area of software security.
ITS4: a static vulnerability scanner for C and C++ code. In Proceedings 16th Annual Computer Security Applications Conference (ACSAC’00) . 257–267
John Viega, J.T. Bloch, Yoshi Kohno, and Gary McGraw. 2000 · 2000
Earlier work this paper cites.
On the value of static analysis for fault detection in software
Jiang Zheng, Laurie Williams, Nachiappan Nagappan, Will Snipes, John P Hudepohl, and Mladen A Vouk. 2006 · 2006
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, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. 2021 · 2009
Earlier work this paper cites.
FlawFinder: A Modular System for Predicting Quality Flaws in Wikipedia.. In CLEF (Online Working Notes/Labs/Workshop) . 1–10
Oliver Ferschke, Iryna Gurevych, and Marc Rittberger. 2012 · 2012
Earlier work this paper cites.
Generalized vulnerability extrapolation using abstract syntax trees. In Proceedings of the 28th Annual Computer Security Applications Conference (Orlando, Florida, USA) (ACSAC ’12) . Association for Computing Machinery, New York, NY, USA, 359–368
Fabian Yamaguchi, Markus Lottmann, and Konrad Rieck. 2012 · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Introduction to software testing
Paul Ammann and Jeff Offutt. 2016 · 2016
Earlier work this paper cites.
Vulnerability Discovery with Function Representation Learning from Unlabeled Projects. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (Dallas, Texas, USA) (CCS ’17) . Association for Computing Machinery, New York, NY, USA, 2539–2541
Guanjun Lin, Jun Zhang, Wei Luo, Lei Pan, and Yang Xiang. 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.
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. In Proceedings 2018 Network and Distributed System Security Symposium (NDSS 2018) . Internet Society
Zhen Li, Deqing Zou, Shouhuai Xu, Xinyu Ou, Hai Jin, Sujuan Wang, Zhijun Deng, and Yuyi Zhong. 2018 · 2018
Earlier work this paper cites.
Parameter-Efficient Transfer Learning for NLP. In Proceedings of the 36th International Conference on Machine Learning (ICML)
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
Earlier work this paper cites.
A Novel Neural Source Code Representation Based on Abstract Syntax Tree. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . 783–794
Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, and Xudong 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 , Trevor Cohn, Yulan He, and Yang Liu (Eds.). 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. 2020 · 2020
Earlier work this paper cites.
Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree. In 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER) . 261–271
Wenhan Wang, Ge Li, Bo Ma, Xin Xia, and Zhi Jin. 2020 · 2020
Earlier work this paper cites.
The Power of Scale for Parameter-Efficient Prompt Tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 3045–3059
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Earlier work this paper cites.
Prefix-Tuning: Optimizing Continuous Prompts for Generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, Online, 4582–4597
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi. 2021a · 2021
Cited alongside, same era.
UniXcoder: Unified Cross-Modal Pre-training for Code Representation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (Eds.). Association for Computational Linguistics, Dublin, Ireland, 7212–7225
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
Cited alongside, same era.
Evaluating Open-Domain Question Answering in the Era of Large Language Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (Eds.). Association for Computational Linguistics, Toronto, Canada, 5591–5606
Ehsan Kamalloo, Nouha Dziri, Charles Clarke, and Davood Rafiei. 2023 · 2023
Closest in time.
Not The End of Story: An Evaluation of ChatGPT-Driven Vulnerability Description Mappings. In Findings of the Association for Computational Linguistics: ACL 2023 , Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (Eds.). Association for Computational Linguistics, Toronto, Canada, 3724–3731
Xin Liu, Yuan Tan, Zhenghang Xiao, Jianwei Zhuge, and Rui Zhou. 2023 · 2023
Closest in time.
Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation. In Proc. of ICML
Anil Mishra and Supriya Gupta. 2023 · 2023
Closest in time.
DIVAS: An LLM-based End-to-End Framework for SoC Security Analysis and Policy-based Protection
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
LoRA: Low-Rank Adaptation of Large Language Models. In Proceedings of the International Conference on Learning Representations
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
Enhancing Vulnerability Detection by Learning from Syntax-Based Execution Paths of Code. In Proc. of ICSE
Dongwoo Kim and Jaewoo Choi. 2022 · 2022
Cited alongside, same era.
Transformer-Based Language Models for Software Vulnerability Detection. In Proceedings of the 38th Annual Computer Security Applications Conference (<conf-loc>, <city>Austin</city>, <state>TX</state>, <country>USA</country>, </conf-loc>) (ACSAC ’22) . Association for Computing Machinery, New York, NY, USA, 481–496
Chandra Thapa, Seung Ick Jang, Muhammad Ejaz Ahmed, Seyit Camtepe, Josef Pieprzyk, and Surya Nepal. 2022 · 2022
Cited alongside, same era.
Unified abstract syntax tree representation learning for cross-language program classification. In Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension (Virtual Event) (ICPC ’22) . Association for Computing Machinery, New York, NY, USA, 390–400
Kesu Wang, Meng Yan, He Zhang, and Haibo Hu. 2022 · 2022
Cited alongside, same era.
A Systematic Evaluation of Large Language Models of Code
Frank F. Xu, Uri Alon, Graham Neubig, and Vincent J. Hellendoorn. 2022 · 2022
Cited alongside, same era.
Fuzzing: a survey for roadmap
Xiaogang Zhu, Sheng Wen, Seyit Camtepe, and Yang Xiang. 2022 · 2022
Cited alongside, same era.
Fixing Hardware Security Bugs with Large Language Models
Baleegh Ahmad, Shailja Thakur, Benjamin Tan, Ramesh Karri, and Hammond Pearce. 2023 · 2023
Cited alongside, same era.
CodeTF: A Transformer-based Library for CodeLLM and Code Intelligence
Nghi D. Q. Bui, Henry Le, Yue Wang, Akhilesh Deepak Gotmare, Junnan Li, and Steven Hoi. 2023 · 2023
Cited alongside, same era.
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 · 2023
Cited alongside, same era.
Sudipta Paria, Aritra Dasgupta, and Swarup Bhunia. 2023 · 2023
Closest in time.
Examining Zero-Shot Vulnerability Repair with Large Language Models. In Proceedings of the 2023 IEEE Symposium on Security and Privacy (SP) . IEEE Computer Society, Los Alamitos, CA, USA, 2339–2356
Hammond Pearce, Benjamin Tan, Baleegh Ahmad, Ramesh Karri, and Brendan Dolan-Gavitt. 2023 · 2023
Closest in time.
Software Vulnerability Detection using Large Language Models. In 2023 IEEE 34th International Symposium on Software Reliability Engineering Workshops (ISSREW) . 112–119
Moumita Das Purba, Arpita Ghosh, Benjamin J. Radford, and Bill Chu. 2023 · 2023
Closest in time.
LlaMA: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Closest in time.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023 · 2023
Closest in time.
Automated Software Testing Starting from Static Analysis: Current State of the Art
Yan Wu, Jingyi Su, David D. Moran, and Chris D. Near. 2023 · 2023
Closest in time.
DLAP: A Deep Learning Augmented Prompting Framework for Vulnerability Detection. In Proc. of NeurIPS
Kaizhi Yu and Yixuan Chen. 2023 · 2023
Closest in time.
Large Language Models Meet NL2Code: A Survey. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL) , Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (Eds.)
Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, and Jian-Guang Lou. 2023 · 2023
Closest in time.
Extractive Summarization via ChatGPT for Faithful Summary Generation. In Findings of the Association for Computational Linguistics: EMNLP 2023 , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 3270–3278
Haopeng Zhang, Xiao Liu, and Jiawei Zhang. 2023b · 2023
Closest in time.
EcoAssistant: Using LLM Assistant More Affordably and Accurately
Jieyu Zhang, Ranjay Krishna, Ahmed H Awadallah, and Chi Wang. 2023a · 2023
Closest in time.
Vulnerability Detection with Code Language Models: How Far Are We?
Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim, Chawin Sitawarin, Xinyun Chen, Basel Alomair, David Wagner, Baishakhi Ray, and Yizheng Chen. 2024 · 2024
Closest in time.
Fixing security vulnerabilities with AI
Tiferet Gazit. 2024 · 2024
Closest in time.