VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs

Training with Noise is Equivalent to Tikhonov Regularization

Chris M. Bishop. 1995 · 1995

Earlier work this paper cites.

ANTLR: A predicated-LL(k) parser generator

T. J. Parr and R. W. Quong. 1995 · 1995

Earlier work this paper cites.

Noise modelling and evaluating learning from examples

Ray J. Hickey. 1996 · 1996

Earlier work this paper cites.

SMOTE: synthetic minority over-sampling technique

Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. 2002 · 2002

Earlier work this paper cites.

CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Original

Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020 · 2002

Earlier work this paper cites.

The Probabilistic Relevance Framework: BM25 and Beyond

Stephen Robertson and Hugo Zaragoza. 2009 · 2009

Earlier work this paper cites.

Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation

David Powers. 2011 · 2011

Earlier work this paper cites.

Classification in the Presence of Label Noise: A Survey

Benoit Frenay and Michel Verleysen. 2014 · 2013

Earlier work this paper cites.

C-brain: a deep learning accelerator that tames the diversity of CNNs through adaptive data-level parallelization. In Proceedings of the 53rd Annual Design Automation Conference (Austin, Texas) (DAC ’16) . Association for Computing Machinery, New York, NY, USA, Article 123, 6 pages

Lili Song, Ying Wang, Yinhe Han, Xin Zhao, Bosheng Liu, and Xiaowei Li. 2016 · 2016

Earlier work this paper cites.

Improved Regularization of Convolutional Neural Networks with Cutout

Original

Terrance DeVries and Graham W. Taylor. 2017 · 2017

Earlier work this paper cites.

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. In 25th Annual Network and Distributed System Security Symposium, NDSS 2018, San Diego, California, USA, February 18-21, 2018 . The 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.

mixup: Beyond Empirical Risk Minimization. In International Conference on Learning Representations

Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. 2018 · 2018

Earlier work this paper cites.

Diversity in Machine Learning

Zhiqiang Gong, Ping Zhong, and Weidong Hu. 2019 · 2019

Earlier work this paper cites.

Augmenting Data with Mixup for Sentence Classification: An Empirical Study

Original

Hongyu Guo, Yongyi Mao, and Richong Zhang. 2019 · 2019

Earlier work this paper cites.

Deep Learning-Based Vulnerable Function Detection: A Benchmark. In Information and Communications Security: 21st International Conference, ICICS 2019, Beijing, China, December 15–17, 2019, Revised Selected Papers (Beijing, China). Springer-Verlag, Berlin, Heidelberg, 219–232

Guanjun Lin, Wei Xiao, Jun Zhang, and Yang Xiang. 2019 · 2019

Earlier work this paper cites.

Impact of Discretization Noise of the Dependent Variable on Machine Learning Classifiers in Software Engineering

Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei, and Ahmed E. Hassan. 2021 · 2019

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 , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc

Original

Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu. 2019 · 2019

Earlier work this paper cites.

Deep Learning Based Vulnerability Detection: Are We There Yet?

Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, and Baishakhi Ray. 2020 · 2020

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.

Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21) . Association for Computing Machinery, New York, NY, USA, 511–521

Nghi D. Q. Bui, Yijun Yu, and Lingxiao Jiang. 2021 · 2021

Cited alongside, same era.

Vulnerability detection with fine-grained interpretations. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Athens, Greece) (ESEC/FSE 2021) . Association for Computing Machinery, New York, NY, USA, 292–303

Yi Li, Shaohua Wang, and Tien N. Nguyen. 2021 · 2021

Cited alongside, same era.

The Impact of Feature Importance Methods on the Interpretation of Defect Classifiers

Gopi Krishnan Rajbahadur, Shaowei Wang, Gustavo A. Oliva, Yasutaka Kamei, and Ahmed E. Hassan. 2022 · 2021

Cited alongside, same era.

Learning Structural Edits via Incremental Tree Transformations. In International Conference on Learning Representations

VULGEN: Realistic Vulnerability Generation Via Pattern Mining and Deep Learning. In Proceedings of the 45th International Conference on Software Engineering (Melbourne, Victoria, Australia) (ICSE ’23) . IEEE Press, 2527–2539

Yu Nong, Yuzhe Ou, Michael Pradel, Feng Chen, and Haipeng Cai. 2023 · 2023

Later among the works it cites.

Introducing ChatGPT

OpenAI. 2022 · 2023

Later among the works it cites.

An Analysis of the Automatic Bug Fixing Performance of ChatGPT. In 2023 IEEE/ACM International Workshop on Automated Program Repair (APR) . 23–30

Dominik Sobania, Martin Briesch, Carol Hanna, and Justyna Petke. 2023 · 2023

Later among the works it cites.

DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection. In Proceedings of the 45th International Conference on Software Engineering (Melbourne, Victoria, Australia) (ICSE ’23) . IEEE Press, 2249–2261

Wenbo Wang, Tien N. Nguyen, Shaohua Wang, Yi Li, Jiyuan Zhang, and Aashish Yadavally. 2023b · 2023

Later among the works it cites.