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With the increase in software vulnerabilities that cause significant economic and social losses, automatic vulnerability detection has become essential in software development and maintenance.
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A Sword with Two Edges: Propagation Studies on Both Positive and Negative Information in Online Social Networks
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Toward Large-Scale Vulnerability Discovery using Machine Learning. In CODASPY . 85–96
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VulDetector: Detecting Vulnerabilities Using Weighted Feature Graph Comparison
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Automatic feature learning for vulnerability prediction
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POSTER: Vulnerability Discovery with Function Representation Learning from Unlabeled Projects. In CCS . 2539–2541
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Defect Prediction in Android Binary Executables Using Deep Neural Network
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CoBOT: static C/C++ bug detection in the presence of incomplete code. In ICPC . 385–388
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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, Marc W. McConley, Jeffrey M. Opper, Peter Chin, and Tomo Lazovich. 2018 · 2018
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VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. In NDSS
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Cross-Project Transfer Representation Learning for Vulnerable Function Discovery
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Automated Vulnerability Detection in Source Code Using Deep Representation Learning. In ICMLA . 757–762
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Towards security defect prediction with AI
Carson D. Sestili, William S. Snavely, and Nathan M. VanHoudnos. 2018 · 2018
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Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection. In ICLR
Tue Le, Tuan Nguyen, Trung Le, Dinh Q. Phung, Paul Montague, Olivier Y. de Vel, and Lizhen Qu. 2019 · 2019
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Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. In NeurIPS . 10197–10207
Yaqin Zhou, Shangqing Liu, Jing Kai Siow, Xiaoning Du, and Yang Liu. 2019 · 2019
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How Can We Know What Language Models Know
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Software Vulnerability Detection Using Deep Neural Networks: A Survey
Guanjun Lin, Sheng Wen, Qing-Long Han, Jun Zhang, and Yang Xiang. 2020 · 2020
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Training Compute-Optimal Large Language Models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre. 2022 · 2022
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SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
Zhen Li, Deqing Zou, Shouhuai Xu, Hai Jin, Yawei Zhu, and Zhaoxuan Chen. 2022 · 2022
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An empirical study on the effectiveness of static C code analyzers for vulnerability detection. In ISSTA . 544–555
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What Makes Good In-Context Examples for GPT-3?. In DeeLIO@ACL . 100–114
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Design Guidelines for Prompt Engineering Text-to-Image Generative Models. In CHI . 384:1–384:23
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. In EMNLP (1) . 4222–4235
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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On the Opportunities and Risks of Foundation Models
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Making Pre-trained Language Models Better Few-shot Learners. In ACL/IJCNLP (1) . 3816–3830
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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GraphCodeBERT: Pre-training Code Representations with Data Flow. In ICLR
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 B. Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. 2021 · 2021
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Checkmarx
Israel. 2021 · 2021
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The Power of Scale for Parameter-Efficient Prompt Tuning. In EMNLP (1) . 3045–3059
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Prefix-Tuning: Optimizing Continuous Prompts for Generation. In ACL/IJCNLP (1) . 4582–4597
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Vivian Liu and Lydia B. Chilton. 2022 · 2022
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ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection. In ICSE-Companion . 178–182
Van-Anh Nguyen, Dai Quoc Nguyen, Van Nguyen, Trung Le, Quan Hung Tran, and Dinh Phung. 2022 · 2022
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Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew K. Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakas, and et al. 2022 · 2022
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In NeurIPS , Vol. 35. 24824–24837
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022 · 2022
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OPT: Open Pre-trained Transformer Language Models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona T. Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022 · 2022
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Learning to Prompt for Vision-Language Models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. 2022 · 2022
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Rohan Anil, Andrew M. Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, Eric Chu, Jonathan H. Clark, Laurent El Shafey, Yanping Huang, Kathy Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernández Ábrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan A. Botha, James Bradbury, Siddhartha Brahma, Kevin Brooks, Michele Catasta, Yong Cheng, Colin Cherry, Christopher A. Choquette-Choo, Aakanksha Chowdhery, Clément Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, Mark Díaz, Nan Du, Ethan Dyer, Vladimir Feinberg, Fangxiaoyu Feng, Vlad Fienber, Markus Freitag, Xavier Garcia, Sebastian Gehrmann, Lucas Gonzalez, and et al. 2023 · 2023
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A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repair
Jialun Cao, Meiziniu Li, Ming Wen, and Shing-Chi Cheung. 2023 · 2023
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Improving ChatGPT Prompt for Code Generation
Chao Liu, Xuanlin Bao, Hongyu Zhang, Neng Zhang, Haibo Hu, Xiaohong Zhang, and Meng Yan. 2023a · 2023
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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. 2023b · 2023
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WizardCoder: Empowering Code Large Language Models with Evol-Instruct
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Commit-Based Class-Level Defect Prediction for Python Projects
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Boosted Prompt Ensembles for Large Language Models
Silviu Pitis, Michael R. Zhang, Andrew Wang, and Jimmy Ba. 2023 · 2023
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An Analysis of the Automatic Bug Fixing Performance of ChatGPT
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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, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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GLM-130B: An Open Bilingual Pre-trained Model. In ICLR
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A Survey of Large Language Models
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