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Recent advances in large language models (LLMs) significantly boost their usage in software engineering.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
Dynamic testing
Elena L Grigorenko and Robert J Sternberg. 1998 · 1998
Earlier work this paper cites.
Expectations, outcomes, and challenges of modern code review. In 2013 35th International Conference on Software Engineering (ICSE)
Alberto Bacchelli and Christian Bird. 2013 · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
Earlier work this paper cites.
The Impact of Code Review Coverage and Code Review Participation on Software Quality: A Case Study of the Qt, VTK, and ITK Projects (MSR 2014)
Shane McIntosh, Yasutaka Kamei, Bram Adams, and Ahmed E. Hassan. 2014 · 2014
Earlier work this paper cites.
DeepCoder: Learning to Write Programs. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . OpenReview.net
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow. 2017 · 2017
Earlier work this paper cites.
Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow. In International Conference on Mining Software Repositories (MSR) . ACM
Pengcheng Yin, Bowen Deng, Edgar Chen, Bogdan Vasilescu, and Graham Neubig. 2018 · 2018
Earlier work this paper cites.
Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019 · 2019
Earlier work this paper cites.
Knockoff nets: Stealing functionality of black-box models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz. 2019 · 2019
Earlier work this paper cites.
Code generation as a dual task of code summarization
Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, and Zhi Jin. 2019 · 2019
Earlier work this paper cites.
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. In NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada , Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d’Alché-Buc, Emily B. Fox, and Roman Garnett (Eds.)
Yaqin Zhou, Shangqing Liu, Jing Kai Siow, Xiaoning Du, and Yang Liu. 2019 · 2019
Earlier work this paper cites.
Exploring connections between active learning and model extraction. In Proceedings of the 29th USENIX Conference on Security Symposium
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, and Songbai Yan. 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, Online Event, 16-20 November 2020 (Findings of ACL, Vol. EMNLP 2020) , Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics
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.
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. 2020 · 2020
Earlier work this paper cites.
High accuracy and high fidelity extraction of neural networks. In Proceedings of the 29th USENIX Conference on Security Symposium
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, and Nicolas Papernot. 2020 · 2020
Earlier work this paper cites.
Symbolic execution with SymCC: Don’t interpret, compile!. In Proceedings of the 29th USENIX Conference on Security Symposium . 181–198
Sebastian Poeplau and Aurélien Francillon. 2020 · 2020
Earlier work this paper cites.
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, and Peter J. Liu. 2020 · 2020
Earlier work this paper cites.
Codebleu: a method for automatic evaluation of code synthesis
Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, and Shuai Ma. 2020 · 2020
Earlier work this paper cites.
Unsupervised translation of programming languages
Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample. 2020 · 2020
Earlier work this paper cites.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. In EMNLP 2020, Online, November 16-20, 2020 . Association for Computational Linguistics
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Earlier work this paper cites.
Primers or Reminders? The Effects of Existing Review Comments on Code Review (ICSE ’20) . Association for Computing Machinery, New York, NY, USA
Davide Spadini, Gül Çalikli, and Alberto Bacchelli. 2020 · 2020
Earlier work this paper cites.
Imitation Attacks and Defenses for Black-box Machine Translation Systems. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020 , Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics
Eric Wallace, Mitchell Stern, and Dawn Song. 2020 · 2020
Earlier work this paper cites.
CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples.. In NDSS
Honggang Yu, Kaichen Yang, Teng Zhang, Yun-Yun Tsai, Tsung-Yi Ho, and Yier Jin. 2020 · 2020
Earlier work this paper cites.
Unified Pre-training for Program Understanding and Generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021 , Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tür, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou (Eds.). Association for Computational Linguistics
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 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, et al · 2021
Earlier work this paper cites.
Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Xuanli He, Lingjuan Lyu, Lichao Sun, and Qiongkai Xu. 2021 · 2021
Cited alongside, same era.
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation. In NeurIPS Datasets and Benchmarks 2021, virtual , Joaquin Vanschoren and Sai-Kit Yeung (Eds.)
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin B. Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and Shujie Liu. 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 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021 , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics
Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi. 2021 · 2021
Cited alongside, same era.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022b · 2022
Later among the works it cites.
How Important are Good Method Names in Neural Code Generation? A Model Robustness Perspective
Guang Yang, Yu Zhou, Wenhua Yang, Tao Yue, Xiang Chen, and Taolue Chen. 2022 · 2022
Later among the works it cites.
XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence
Ming Zhu, Aneesh Jain, Karthik Suresh, Roshan Ravindran, Sindhu Tipirneni, and Chandan K Reddy. 2022 · 2022
Later among the works it cites.
Productivity assessment of neural code completion. In MAPS@PLDI 2022: 6th ACM SIGPLAN International Symposium on Machine Programming, San Diego, CA, USA, 13 June 2022 , Swarat Chaudhuri and Charles Sutton (Eds.)
Albert Ziegler, Eirini Kalliamvakou, X. Alice Li, Andrew Rice, Devon Rifkin, Shawn Simister, Ganesh Sittampalam, and Edward Aftandilian. 2022 · 2022
Later among the works it cites.
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Beyond model extraction: Imitation attack for black-box nlp apis
Qiongkai Xu, Xuanli He, Lingjuan Lyu, Lizhen Qu, and Gholamreza Haffari. 2021a · 2021
Cited alongside, same era.
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs
Qiongkai Xu, Xuanli He, Lingjuan Lyu, Lizhen Qu, and Gholamreza Haffari. 2021b · 2021
Cited alongside, same era.
Michael Bommarito II and Daniel Martin Katz. 2022 · 2022
Cited alongside, same era.
Improving In-Context Few-Shot Learning via Self-Supervised Training. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022
Mingda Chen, Jingfei Du, Ramakanth Pasunuru, Todor Mihaylov, Srini Iyer, Veselin Stoyanov, and Zornitsa Kozareva. 2022a · 2022
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
Cited alongside, same era.
Protecting Intellectual Property of Language Generation APIs with Lexical Watermark
Xuanli He, Qiongkai Xu, Lingjuan Lyu, Fangzhao Wu, and Chenguang Wang. 2022a · 2022
Cited alongside, same era.
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks
Xuanli He, Qiongkai Xu, Yi Zeng, Lingjuan Lyu, Fangzhao Wu, Jiwei Li, and Ruoxi Jia. 2022b · 2022
Cited alongside, same era.
Large Language Models Are Reasoning Teachers
Namgyu Ho, Laura Schmid, and Se-Young Yun. 2022 · 2022
Cited alongside, same era.
Codeattack: Code-based adversarial attacks for pre-trained programming language models
Akshita Jha and Chandan K Reddy. 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
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GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo
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Sparks of artificial general intelligence: Early experiments with gpt-4
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StarCoder: may the source be with you!
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CCTEST: Testing and Repairing Code Completion Systems. In 45th IEEE/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, May 14-20, 2023 . IEEE, 1238–1250
Zongjie Li, Chaozheng Wang, Zhibo Liu, Haoxuan Wang, Dong Chen, Shuai Wang, and Cuiyun Gao. 2023b · 2023
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Protecting Intellectual Property of Large Language Model-Based Code Generation APIs via Watermarks. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, CCS 2023, Copenhagen, Denmark, November 26-30, 2023
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Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
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WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2023 · 2023
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An Empirical Comparison of Pre-Trained Models of Source Code. In Proceedings of the 45th International Conference on Software Engineering (Melbourne, Victoria, Australia) (ICSE ’23) . 2136–2148
Changan Niu, Chuanyi Li, Vincent Ng, Dongxiao Chen, Jidong Ge, and Bin Luo. 2023 · 2023
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Our approach to AI safety
OpenAI. 2023 · 2023
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Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
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On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, et al · 2023
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Self-Consistency Improves Chain of Thought Reasoning in Language Models. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023c · 2023
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Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Raghavi Chandu, David Wadden, Kelsey MacMillan, Noah A Smith, Iz Beltagy, et al · 2023
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Jules White, Sam Hays, Quchen Fu, Jesse Spencer-Smith, and Douglas C Schmidt. 2023 · 2023
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Conversational automated program repair
Chunqiu Steven Xia and Lingming Zhang. 2023 · 2023
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Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2023
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Universal and Transferable Adversarial Attacks on Aligned Language Models
Andy Zou, Zifan Wang, J. Zico Kolter, and Matt Fredrikson. 2023 · 2023
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