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Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc.
Pre-trained Language Model Representations for Language Generation
Sergey Edunov, Alexei Baevski, and Michael Auli. 2019 · 1903
Earlier work this paper cites.
MASS: Masked Sequence to Sequence Pre-training for Language Generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019 · 1905
Earlier work this paper cites.
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. In EMNLP
Jamaal Hay Wenpeng Yin and Dan Roth. 2019 · 1909
Earlier work this paper cites.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
Earlier work this paper cites.
ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
Dongling Xiao, Han Zhang, Yukun Li, Yu Sun, Hao Tian, Hua Wu, and Haifeng Wang. 2020 · 2001
Earlier work this paper cites.
The Mathematics of Statistical Machine Translation: Parameter Estimation
Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993 · 2003
Earlier work this paper cites.
Pre-trained Models for Natural Language Processing: A Survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. 2020 · 2003
Earlier work this paper cites.
Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
Frank F. Xu, Zhengbao Jiang, Pengcheng Yin, Bogdan Vasilescu, and Graham Neubig. 2020 · 2004
Earlier work this paper cites.
A pre-training technique to localize medical BERT and enhance BioBERT
Shoya Wada, Toshihiro Takeda, Shiro Manabe, Shozo Konishi, Jun Kamohara, and Yasushi Matsumura. 2020 · 2005
Earlier work this paper cites.
MAPO: Mining API Usages from Open Source Repositories. In Proceedings of the 2006 International Workshop on Mining Software Repositories (Shanghai, China) (MSR ’06) . Association for Computing Machinery, New York, NY, USA, 54–57
Tao Xie and Jian Pei. 2006 · 2006
Earlier work this paper cites.
Sourcerer: Mining and Searching Internet-Scale Software Repositories
Erik Linstead, Sushil Bajracharya, Trung Ngo, Paul Rigor, Cristina Lopes, and Pierre Baldi. 2009 · 2009
Earlier work this paper cites.
Towards an Intelligent Code Search Engine (AAAI’10) . AAAI Press, 1358–1363
Jinhan Kim, Sanghoon Lee, Seung-won Hwang, and Sunghun Kim. 2010 · 2010
Earlier work this paper cites.
Portfolio: Searching for Relevant Functions and Their Usages in Millions of Lines of Code
Collin Mcmillan, Denys Poshyvanyk, Mark Grechanik, Qing Xie, and Chen Fu. 2013 · 2013
Earlier work this paper cites.
Mining succinct and high-coverage API usage patterns from source code. In 2013 10th Working Conference on Mining Software Repositories (MSR) . 319–328
Jue Wang, Yingnong Dang, Hongyu Zhang, Kai Chen, Tao Xie, and Dongmei Zhang. 2013 · 2013
Earlier work this paper cites.
CodeHow: Effective Code Search Based on API Understanding and Extended Boolean Model (E). In 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) . 260–270
Fei Lv, Hongyu Zhang, Jian-guang Lou, Shaowei Wang, Dongmei Zhang, and Jianjun Zhao. 2015 · 2015
Earlier work this paper cites.
UP-Miner: A utility pattern mining toolbox. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW) . IEEE, 1656–1659
Vincent S Tseng, Cheng-Wei Wu, Jun-Han Lin, and Philippe Fournier-Viger. 2015 · 2015
Earlier work this paper cites.
Parameter-Free Probabilistic API Mining across GitHub. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (Seattle, WA, USA) (FSE 2016) . Association for Computing Machinery, New York, NY, USA, 254–265
Jaroslav Fowkes and Charles Sutton. 2016 · 2016
Earlier work this paper cites.
Deep API Learning. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (Seattle, WA, USA) (FSE 2016) . Association for Computing Machinery, New York, NY, USA, 631–642
Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, and Sunghun Kim. 2016 · 2016
Earlier work this paper cites.
Latent Predictor Networks for Code Generation. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers . The Association for Computer Linguistics
Wang Ling, Phil Blunsom, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Fumin Wang, and Andrew W. Senior. 2016 · 2016
Cited alongside, same era.
SWIM: Synthesizing What i Mean: Code Search and Idiomatic Snippet Synthesis (ICSE ’16) . Association for Computing Machinery, New York, NY, USA, 357–367
Mukund Raghothaman, Yi Wei, and Youssef Hamadi. 2016 · 2016
Cited alongside, same era.
Language Generation with Recurrent Generative Adversarial Networks without Pre-training
Ofir Press, Amir Bar, Ben Bogin, Jonathan Berant, and Lior Wolf. 2017 · 2017
Cited alongside, same era.
Abstract Syntax Networks for Code Generation and Semantic Parsing. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers , Regina Barzilay and Min-Yen Kan (Eds.). Association for Computational Linguistics, 1139–1149
Improving Code Search with Co-Attentive Representation Learning. In ICPC ’20: 28th International Conference on Program Comprehension, Seoul, Republic of Korea, July 13-15, 2020 . ACM, 196–207
Jianhang Shuai, Ling Xu, Chao Liu, Meng Yan, Xin Xia, and Yan Lei. 2020 · 2020
Later among the works it cites.
TreeGen: A Tree-Based Transformer Architecture for Code Generation. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020 . AAAI Press, 8984–8991
Zeyu Sun, Qihao Zhu, Yingfei Xiong, Yican Sun, Lili Mou, and Lu Zhang. 2020 · 2020
Later among the works it cites.
IntelliCode Compose: Code Generation Using Transformer
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan. 2020 · 2020
Later among the works it cites.
Sentiment Analysis for Software Engineering: How Far Can Pre-trained Transformer Models Go?. In 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME) . 70–80
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Maxim Rabinovich, Mitchell Stern, and Dan Klein. 2017 · 2017
Cited alongside, same era.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
A Syntactic Neural Model for General-Purpose Code Generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers , Regina Barzilay and Min-Yen Kan (Eds.). Association for Computational Linguistics, 440–450
Pengcheng Yin and Graham Neubig. 2017 · 2017
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.
Deep code search. In Proceedings of the 40th International Conference on Software Engineering, ICSE 2018, Gothenburg, Sweden, May 27 - June 03, 2018 , Michel Chaudron, Ivica Crnkovic, Marsha Chechik, and Mark Harman (Eds.). ACM, 933–944
Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. 2018 · 2018
Cited alongside, same era.
Scaling Neural Machine Translation
Myle Ott, Sergey Edunov, David Grangier, and Michael Auli. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Program Synthesis and Semantic Parsing with Learned Code Idioms. 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.). 10824–10834
Eui Chul Richard Shin, Miltiadis Allamanis, Marc Brockschmidt, and Alex Polozov. 2019 · 2019
Cited alongside, same era.
Ting Zhang, Bowen Xu, Ferdian Thung, Stefanus Agus Haryono, David Lo, and Lingxiao Jiang. 2020 · 2020
Later among the works it cites.
Unified Pre-training for Program Understanding and Generation
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Later among the works it cites.
Pre-Trained Neural Language Models for Automatic Mobile App User Feedback Answer Generation. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) . 120–125
Yue Cao and Fatemeh H. Fard. 2021 · 2021
Later among the works it cites.
Multimodal Representation for Neural Code Search. In IEEE International Conference on Software Maintenance and Evolution, ICSME 2021, Luxembourg, September 27 - October 1, 2021 . IEEE, 483–494
Jian Gu, Zimin Chen, and Martin Monperrus. 2021 · 2021
Later among the works it cites.
Mohammad Abdul Hadi and Fatemeh Hendijani Fard. 2021 · 2021
Later among the works it cites.
FACOS: Finding API Relevant Contents on Stack Overflow with Semantic and Syntactic Analysis
Kien Luong, Mohammad Hadi, Ferdian Thung, Fatemeh Fard, and David Lo. 2021 · 2021
Later among the works it cites.
DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders
Shuming Ma, Li Dong, Shaohan Huang, Dongdong Zhang, Alexandre Muzio, Saksham Singhal, Hany Hassan Awadalla, Xia Song, and Furu Wei. 2021 · 2021
Later among the works it cites.
Automatic Code Generation using Pre-Trained Language Models
Luis Perez, Lizi Ottens, and Sudharshan Viswanathan. 2021 · 2021
Later among the works it cites.
CoTexT: Multi-task Learning with Code-Text Transformer
Long N. Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James T. Anibal, Alec Peltekian, and Yanfang Ye. 2021 · 2021
Later among the works it cites.
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
Weizhen Qi, Yeyun Gong, Yu Yan, Can Xu, Bolun Yao, Bartuer Zhou, Biao Cheng, Daxin Jiang, Jiusheng Chen, Ruofei Zhang, Houqiang Li, and Nan Duan. 2021 · 2021
Later among the works it cites.
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation
Yunfan Shao, Zhichao Geng, Yitao Liu, Junqi Dai, Fei Yang, Li Zhe, Hujun Bao, and Xipeng Qiu. 2021 · 2021
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A Survey of Automatic Code Generation from Natural Language
Jiho Shin and Jaechang Nam. 2021 · 2021
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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, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals. 2022 · 2022
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Synchromesh: Reliable code generation from pre-trained language models
Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani. 2022 · 2022
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