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Developers often perform repetitive code editing activities for various reasons (e.g., code refactoring) during software development.
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
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 · 1909
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics . 311–318
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Impact of peer code review on peer impression formation: A survey. In 2013 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement . IEEE, 133–142
Amiangshu Bosu and Jeffrey C Carver. 2013 · 2013
Earlier work this paper cites.
A study of repetitiveness of code changes in software evolution. In 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 180–190
Hoan Anh Nguyen, Anh Tuan Nguyen, Tung Thanh Nguyen, Tien N Nguyen, and Hridesh Rajan. 2013 · 2013
Earlier work this paper cites.
Detecting and characterizing semantic inconsistencies in ported code. In 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 367–377
Baishakhi Ray, Miryung Kim, Suzette Person, and Neha Rungta. 2013 · 2013
Earlier work this paper cites.
API code recommendation using statistical learning from fine-grained changes. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering . 511–522
Anh Tuan Nguyen, Michael Hilton, Mihai Codoban, Hoan Anh Nguyen, Lily Mast, Eli Rademacher, Tien N Nguyen, and Danny Dig. 2016 · 2016
Earlier work this paper cites.
Neural Machine Translation of Rare Words with Subword Units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 1715–1725
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (Demo Track)
Pengcheng Yin and Graham Neubig. 2018 · 2018
Earlier work this paper cites.
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. In International Conference on Learning Representations
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning. 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, Volume 1 (Long and Short Papers) . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Earlier work this paper cites.
Energy and Policy Considerations for Deep Learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 3645–3650
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
Earlier work this paper cites.
On learning meaningful code changes via neural machine translation. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 25–36
Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota, and Denys Poshyvanyk. 2019 · 2019
Cited alongside, same era.
Codit: Code editing with tree-based neural models
Saikat Chakraborty, Yangruibo Ding, Miltiadis Allamanis, and Baishakhi Ray. 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 . 1536–1547
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
Cited alongside, same era.
GraphCodeBERT: Pre-training Code Representations with Data Flow. In International Conference on Learning Representations
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, LIU Shujie, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al · 2020
Cited alongside, same era.
Towards automating code review activities. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 163–174
Rosalia Tufano, Luca Pascarella, Michele Tufanoy, Denys Poshyvanykz, and Gabriele Bavota. 2021 · 2021
Later among the works it cites.
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 . 8696–8708
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021 · 2021
Later among the works it cites.
Wangchunshu Zhou, Tao Ge, Canwen Xu, Ke Xu, and Furu Wei. 2021 · 2021
Later among the works it cites.
CrystalBLEU: precisely and efficiently measuring the similarity of code. In 37th IEEE/ACM International Conference on Automated Software Engineering . 1–12
Aryaz Eghbali and Michael Pradel. 2022 · 2022
Closest in time.
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BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 7871–7880
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Multi-task learning based pre-trained language model for code completion. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering . 473–485
Fang Liu, Ge Li, Yunfei Zhao, and Zhi Jin. 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. 2020 · 2020
Cited alongside, same era.
Treegen: A tree-based transformer architecture for code generation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 8984–8991
Zeyu Sun, Qihao Zhu, Yingfei Xiong, Yican Sun, Lili Mou, and Lu Zhang. 2020 · 2020
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 . 2655–2668
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Cited alongside, same era.
On Multi-Modal Learning of Editing Source Code. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 443–455
Saikat Chakraborty and Baishakhi Ray. 2021 · 2021
Cited alongside, same era.
Editsum: A retrieve-and-edit framework for source code summarization. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 155–166
Jia Li, Yongmin Li, Ge Li, Xing Hu, Xin Xia, and Zhi Jin. 2021 · 2021
Cited alongside, same era.
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al · 2021
Cited alongside, same era.
Real-world code changes
GitHub. 2022 · 2022
Closest in time.
Poison Attack and Defense on Deep Source Code Processing Models
Jia Li, Zhuo Li, Huangzhao Zhang, Ge Li, Zhi Jin, Xing Hu, and Xin Xia. 2022b · 2022
Closest in time.
CodeRetriever: Unimodal and Bimodal Contrastive Learning
Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, and Nan Duan. 2022a · 2022
Closest in time.
SPT-Code: Sequence-to-Sequence Pre-Training for Learning the Representation of Source Code. In 2022 IEEE/ACM 44st International Conference on Software Engineering (ICSE) . IEEE
Changan Niu, Chuanyi Li, Vincent Ng, Jidong Ge, Liguo Huang, and Bin Luo. 2022 · 2022
Closest in time.
AutoTransform: Automated Code Transformation to Support Modern Code Review Process. In 2022 IEEE/ACM 44st International Conference on Software Engineering (ICSE) . IEEE
Patanamon Thongtanunam, Chanathip Pornprasit, and Chakkrit Tantithamthavorn. 2022 · 2022
Closest in time.
Using pre-trained models to boost code review automation. In Proceedings of the 44th International Conference on Software Engineering . 2291–2302
Rosalia Tufano, Simone Masiero, Antonio Mastropaolo, Luca Pascarella, Denys Poshyvanyk, and Gabriele Bavota. 2022 · 2022
Closest in time.
Enabling Programming Thinking in Large Language Models Toward Code Generation
Jia Li, Ge Li, Yongmin Li, and Zhi Jin. 2023a · 2023
Closest in time.
SkCoder: A Sketch-based Approach for Automatic Code Generation. In 45th IEEE/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, May 14-20, 2023 . IEEE, 2124–2135
Jia Li, Yongmin Li, Ge Li, Zhi Jin, Yiyang Hao, and Xing Hu. 2023b · 2023
Closest in time.
Towards Enhancing In-Context Learning for Code Generation
Jia Li, Yunfei Zhao, Yongmin Li, Ge Li, and Zhi Jin. 2023c · 2023
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