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Semantic understanding of programs has attracted great attention in the community.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. 2019 · 1909
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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 · 2002
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Programl: Graph-based deep learning for program optimization and analysis
Chris Cummins, Zacharias V Fisches, Tal Ben-Nun, Torsten Hoefler, and Hugh Leather. 2020 · 2003
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Fuzzing: brute force vulnerability discovery
Michael Sutton, Adam Greene, and Pedram Amini. 2007 · 2007
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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, et al. 2020 · 2009
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Towards a big data curated benchmark of inter-project code clones
Jeffrey Svajlenko, Judith F Islam, Iman Keivanloo, Chanchal K Roy, and Mohammad Mamun Mia. 2014 · 2014
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Convolutional neural networks over tree structures for programming language processing
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. 2016 · 2016
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Continuous fuzzing with libfuzzer and addresssanitizer
Kosta Serebryany. 2016 · 2016
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Poster: Afl-based fuzzing for java with kelinci
Rody Kersten, Kasper Luckow, and Corina S. Păsăreanu. 2017 · 2017
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Iotfuzzer: Discovering memory corruptions in iot through app-based fuzzing
Jiongyi Chen, Wenrui Diao, Qingchuan Zhao, Chaoshun Zuo, Zhiqiang Lin, XiaoFeng Wang, Wing Cheong Lau, Menghan Sun, Ronghai Yang, and Kehuan Zhang. 2018 · 2018
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Angora: Efficient fuzzing by principled search
Peng Chen and Hao Chen. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Collafl: Path sensitive fuzzing
Shuitao Gan, Chao Zhang, Xiaojun Qin, Xuwen Tu, Kang Li, Zhongyu Pei, and Zuoning Chen. 2018 · 2018
Cited alongside, same era.
Nautilus: Fishing for deep bugs with grammars
Cornelius Aschermann, Tommaso Frassetto, Thorsten Holz, Patrick Jauernig, Ahmad-Reza Sadeghi, and Daniel Teuchert. 2019 · 2019
Cited alongside, same era.
Coverage-based greybox fuzzing as markov chain
M. Böhme, V. Pham, and A. Roychoudhury. 2019 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Integrity: Finding integer errors by targeted fuzzing
Yuyang Rong, Peng Chen, and Hao Chen. 2020 · 2020
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Testing static analyses for precision and soundness
Jubi Taneja, Zhengyang Liu, and John Regehr. 2020 · 2020
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Unified pre-training for program understanding and generation
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
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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 · 2021
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How could neural networks understand programs?
Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, and Tie-Yan Liu. 2021 · 2021
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Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
Cited alongside, same era.
AFL++ : Combining incremental steps of fuzzing research
Andrea Fioraldi, Dominik Maier, Heiko Eißfeldt, and Marc Heuse. 2020 · 2020
Cited alongside, same era.
{ \{ FuzzGen } \} : Automatic fuzzer generation
Kyriakos Ispoglou, Daniel Austin, Vishwath Mohan, and Mathias Payer. 2020 · 2020
Cited alongside, same era.
Learning and evaluating contextual embedding of source code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi. 2020 · 2020
Cited alongside, same era.
A metric learning reality check
Kevin Musgrave, Serge Belongie, and Ser-Nam Lim. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu, et al. 2020 · 2020
Cited alongside, same era.
Reinforcement learning-based hierarchical seed scheduling for greybox fuzzing
Jinghan Wang, Chengyu Song, and Heng Yin. 2021a
Cited in the paper.
Project codenet: A large-scale ai for code dataset for learning a diversity of coding tasks
Ruchir Puri, David S Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladmir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, et al. 2021 · 2021
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Unixcoder: Unified cross-modal pre-training for code representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
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Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al. 2022 · 2022
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Structcoder: Structure-aware transformer for code generation
Sindhu Tipirneni, Ming Zhu, and Chandan K Reddy. 2022 · 2022
Later among the works it cites.
Contrabert: Enhancing code pre-trained models via contrastive learning
Shangqing Liu, Bozhi Wu, Xiaofei Xie, Guozhu Meng, and Yang Liu. 2023 · 2023
Closest in time.
Summarizing source code using a neural attention model
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2016 · 2083
Closest in time.