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In the field of code intelligence, effectively modeling long-range code poses a significant challenge.
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.
Individual comparisons by ranking methods
Frank Wilcoxon. 1992 · 1992
Earlier work this paper cites.
Listening to programmers - Taxonomies and characteristics of comments in operating system code. In 31st International Conference on Software Engineering, ICSE 2009, May 16-24, 2009, Vancouver, Canada, Proceedings . IEEE, 331–341
Yoann Padioleau, Lin Tan, and Yuanyuan Zhou. 2009 · 2009
Earlier work this paper cites.
MAPO: Mining and Recommending API Usage Patterns. In ECOOP 2009 - Object-Oriented Programming, 23rd European Conference, Genoa, Italy, July 6-10, 2009. Proceedings (Lecture Notes in Computer Science, Vol. 5653) , Sophia Drossopoulou (Ed.). Springer, 318–343
Hao Zhong, Tao Xie, Lu Zhang, Jian Pei, and Hong Mei. 2009 · 2009
Earlier work this paper cites.
An empirical study of build maintenance effort. In Proceedings of the 33rd International Conference on Software Engineering, ICSE 2011, Waikiki, Honolulu , HI, USA, May 21-28, 2011 , Richard N. Taylor, Harald C. Gall, and Nenad Medvidovic (Eds.). ACM, 141–150
Shane McIntosh, Bram Adams, Thanh H. D. Nguyen, Yasutaka Kamei, and Ahmed E. Hassan. 2011 · 2011
Earlier work this paper cites.
Mining Java class identifier naming conventions. In 34th International Conference on Software Engineering, ICSE 2012, June 2-9, 2012, Zurich, Switzerland , Martin Glinz, Gail C. Murphy, and Mauro Pezzè (Eds.). IEEE Computer Society, 1641–1643
Simon Butler. 2012 · 2012
Earlier work this paper cites.
Expectations, outcomes, and challenges of modern code review. In 35th International Conference on Software Engineering, ICSE ’13, San Francisco, CA, USA, May 18-26, 2013 , David Notkin, Betty H. C. Cheng, and Klaus Pohl (Eds.). IEEE Computer Society, 712–721
Alberto Bacchelli and Christian Bird. 2013 · 2013
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , Yoshua Bengio and Yann LeCun (Eds.)
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Graph-Based Statistical Language Model for Code. In 37th IEEE/ACM International Conference on Software Engineering, ICSE 2015, Florence, Italy, May 16-24, 2015, Volume 1 , Antonia Bertolino, Gerardo Canfora, and Sebastian G. Elbaum (Eds.). IEEE Computer Society, 858–868
Anh Tuan Nguyen and Tien N. Nguyen. 2015 · 2015
Earlier work this paper cites.
Parameter-free probabilistic API mining across GitHub. In Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2016, Seattle, WA, USA, November 13-18, 2016 , Thomas Zimmermann, Jane Cleland-Huang, and Zhendong Su (Eds.). ACM, 254–265
Jaroslav M. Fowkes and Charles Sutton. 2016 · 2016
Earlier work this paper cites.
An empirical study of the impact of modern code review practices on software quality
Shane McIntosh, Yasutaka Kamei, Bram Adams, and Ahmed E. Hassan. 2016 · 2016
Earlier work this paper cites.
Deep code comment generation. In Proceedings of the 26th Conference on Program Comprehension, ICPC 2018, Gothenburg, Sweden, May 27-28, 2018 , Foutse Khomh, Chanchal K. Roy, and Janet Siegmund (Eds.). ACM, 200–210
Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin. 2018 · 2018
Earlier work this paper cites.
Do developers update their library dependencies? - An empirical study on the impact of security advisories on library migration
Raula Gaikovina Kula, Daniel M. Germán, Ali Ouni, Takashi Ishio, and Katsuro Inoue. 2018 · 2018
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.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Earlier work this paper cites.
Automated Vulnerability Detection in Source Code Using Deep Representation Learning. In 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, Orlando, FL, USA, December 17-20, 2018 , M. Arif Wani, Mehmed M. Kantardzic, Moamar Sayed Mouchaweh, João Gama, and Edwin Lughofer (Eds.). IEEE, 757–762
Rebecca L. Russell, Louis Y. Kim, Lei H. Hamilton, Tomo Lazovich, Jacob Harer, Onur Ozdemir, Paul M. Ellingwood, and Marc W. McConley. 2018 · 2018
Earlier work this paper cites.
When deep learning met code search. In Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/SIGSOFT FSE 2019, Tallinn, Estonia, August 26-30, 2019 , Marlon Dumas, Dietmar Pfahl, Sven Apel, and Alessandra Russo (Eds.). ACM, 964–974
José Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, and Satish Chandra. 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers) , Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
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 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
A novel neural source code representation based on abstract syntax tree. In Proceedings of the 41st International Conference on Software Engineering, ICSE 2019, Montreal, QC, Canada, May 25-31, 2019 , Joanne M. Atlee, Tevfik Bultan, and Jon Whittle (Eds.). IEEE / ACM, 783–794
Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, and Xudong Liu. 2019 · 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 32: Annual Conference on Neural Information Processing Systems 2019, 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.). 10197–10207
Yaqin Zhou, Shangqing Liu, Jing Kai Siow, Xiaoning Du, and Yang Liu. 2019 · 2019
Earlier work this paper cites.
A Transformer-based Approach for Source Code Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020 , Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 4998–5007
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2020 · 2020
Earlier work this paper cites.
A theory of dual channel constraints. In ICSE-NIER 2020: 42nd International Conference on Software Engineering, New Ideas and Emerging Results, Seoul, South Korea, 27 June - 19 July, 2020 , Gregg Rothermel and Doo-Hwan Bae (Eds.). ACM, 25–28
Casey Casalnuovo, Earl T. Barr, Santanu Kumar Dash, Prem Devanbu, and Emily Morgan. 2020 · 2020
Cited alongside, same era.
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, 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
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, and Peter J. Liu. 2020a · 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, and Peter J. Liu. 2020b · 2020
CLEAR: Contrastive Learning for API Recommendation. In 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022 . ACM, 376–387
Moshi Wei, Nima Shiri Harzevili, Yuchao Huang, Junjie Wang, and Song Wang. 2022 · 2022
Later among the works it cites.
Boosting API Recommendation With Implicit Feedback
Yu Zhou, Xinying Yang, Taolue Chen, Zhiqiu Huang, Xiaoxing Ma, and Harald C. Gall. 2022 · 2022
Later among the works it cites.
ChatGPT’s One-year Anniversary: Are Open-Source Large Language Models Catching up?
Hailin Chen, Fangkai Jiao, Xingxuan Li, Chengwei Qin, Mathieu Ravaut, Ruochen Zhao, Caiming Xiong, and Shafiq Joty. 2023b · 2023
Later among the works it cites.
API Usage Recommendation Via Multi-View Heterogeneous Graph Representation Learning
Yujia Chen, Cuiyun Gao, Xiaoxue Ren, Yun Peng, Xin Xia, and Michael R. Lyu. 2023a · 2023
Later among the works it cites.
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Cited alongside, same era.
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, 2655–2668
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Cited alongside, same era.
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy. 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, 4713–4722
Colin B. Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, and Alexey Svyatkovskiy. 2021 · 2021
Cited alongside, same era.
CRaDLe: Deep code retrieval based on semantic Dependency Learning
Wenchao Gu, Zongjie Li, Cuiyun Gao, Chaozheng Wang, Hongyu Zhang, Zenglin Xu, and Michael R. Lyu. 2021 · 2021
Cited alongside, same era.
Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021 , Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 6379–6393
Jian Guan, Xiaoxi Mao, Changjie Fan, Zitao Liu, Wenbiao Ding, and Minlie Huang. 2021 · 2021
Cited alongside, same era.
GraphCodeBERT: Pre-training Code Representations with Data Flow. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 . OpenReview.net
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
Cited alongside, same era.
APIRecX: Cross-Library API Recommendation via Pre-Trained Language Model. 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, 3425–3436
Yuning Kang, Zan Wang, Hongyu Zhang, Junjie Chen, and Hanmo You. 2021 · 2021
Cited alongside, same era.
Code Prediction by Feeding Trees to Transformers. In 43rd IEEE/ACM International Conference on Software Engineering, ICSE 2021, Madrid, Spain, 22-30 May 2021 . IEEE, 150–162
Seohyun Kim, Jinman Zhao, Yuchi Tian, and Satish Chandra. 2021 · 2021
Cited alongside, same era.
Vulnerability detection with fine-grained interpretations. In ESEC/FSE ’21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Athens, Greece, August 23-28, 2021 , Diomidis Spinellis, Georgios Gousios, Marsha Chechik, and Massimiliano Di Penta (Eds.). ACM, 292–303
Yi Li, Shaohua Wang, and Tien N. Nguyen. 2021 · 2021
Cited alongside, same era.
Small Pre-trained Language Models Can be Fine-tuned as Large Models via Over-Parameterization. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023 , Anna Rogers, Jordan L. Boyd-Graber, and Naoaki Okazaki (Eds.). Association for Computational Linguistics, 3819–3834
Ze-Feng Gao, Kun Zhou, Peiyu Liu, Wayne Xin Zhao, and Ji-Rong Wen. 2023 · 2023
Later among the works it cites.
LongCoder: A Long-Range Pre-trained Language Model for Code Completion. In International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA (Proceedings of Machine Learning Research, Vol. 202) , Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (Eds.). PMLR, 12098–12107
Daya Guo, Canwen Xu, Nan Duan, Jian Yin, and Julian J. McAuley. 2023 · 2023
Later among the works it cites.
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. In Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023 , Anna Rogers, Jordan L. Boyd-Graber, and Naoaki Okazaki (Eds.). Association for Computational Linguistics, 8003–8017
Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023 · 2023
Later among the works it cites.
Huggingface Hub. 2023
2023
Later among the works it cites.
Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities
Avishree Khare, Saikat Dutta, Ziyang Li, Alaia Solko-Breslin, Rajeev Alur, and Mayur Naik. 2023 · 2023
Later among the works it cites.
PTM-APIRec: Leveraging Pre-trained Models of Source Code in API Recommendation
Zhihao Li, Chuanyi Li, Ze Tang, Wanhong Huang, Jidong Ge, Bin Luo, Vincent Ng, Ting Wang, Yucheng Hu, and Xiaopeng Zhang. 2023 · 2023
Later among the works it cites.
Github website
Microsoft. 2023 · 2023
Later among the works it cites.
CodeGen2: Lessons for Training LLMs on Programming and Natural Languages
Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, and Yingbo Zhou. 2023a · 2023
Later among the works it cites.
CodeGen2: Lessons for Training LLMs on Programming and Natural Languages
Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, and Yingbo Zhou. 2023b · 2023
Later among the works it cites.
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2023c · 2023
Later among the works it cites.
OpenAI. 2023 · 2023
Later among the works it cites.
Revisiting, Benchmarking and Exploring API Recommendation: How Far Are We?
Yun Peng, Shuqing Li, Wenwei Gu, Yichen Li, Wenxuan Wang, Cuiyun Gao, and Michael R. Lyu. 2023 · 2023
Later among the works it cites.
Llama 2: Open Foundation and Fine-Tuned Chat Models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurélien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
Later among the works it cites.
Tree-sitter. 2023
2023
Later among the works it cites.
Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2023, San Francisco, CA, USA, December 3-9, 2023 , Satish Chandra, Kelly Blincoe, and Paolo Tonella (Eds.). ACM, 172–184
Yuxiang Wei, Chunqiu Steven Xia, and Lingming Zhang. 2023 · 2023
Later among the works it cites.
Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning. In 45th IEEE/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, May 14-20, 2023 . IEEE, 2275–2286
Xin-Cheng Wen, Yupan Chen, Cuiyun Gao, Hongyu Zhang, Jie M. Zhang, and Qing Liao. 2023a · 2023
Later among the works it cites.
When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection. In 38th IEEE/ACM International Conference on Automated Software Engineering, ASE 2023, Luxembourg, September 11-15, 2023 . IEEE, 345–357
Xin-Cheng Wen, Xinchen Wang, Cuiyun Gao, Shaohua Wang, Yang Liu, and Zhaoquan Gu. 2023b · 2023
Later among the works it cites.
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X
Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, and Jie Tang. 2023 · 2023
Later among the works it cites.