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Code review is an essential activity for ensuring the quality and maintainability of software projects.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michatgpt promptsel Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 1910
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Bleu: a Method for Automatic Evaluation of Machine Translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, 311–318
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005
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Interrater reliability: the kappa statistic
Mary L McHugh. 2012 · 2012
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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
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Characteristics of useful code reviews: An empirical study at microsoft. In 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories . IEEE, 146–156
Amiangshu Bosu, Michaela Greiler, and Christian Bird. 2015 · 2015
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Who should review my code? a file location-based code-reviewer recommendation approach for modern code review. In 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER) . IEEE, 141–150
Patanamon Thongtanunam, chatgpt_promptskkrit Tantithamthavorn, Raula Gaikovina Kula, Norihiro Yoshida, Hajimu Iida, and Ken-ichi Matsumoto. 2015 · 2015
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Automatically recommending peer reviewers in modern code review
Motahareh Bahrami Zanjani, Huzefa Kagdi, and Christian Bird. 2015 · 2015
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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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
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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
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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
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Behind the intents: An in-depth empirical study on software refactoring in modern code review. In Proceedings of the 17th International Conference on Mining Software Repositories . 125–136
Matheus Paixão, Anderson Uchôa, Ana Carla Bibiano, Daniel Oliveira, Alessandro Garcia, Jens Krinke, and Emilio Arvonio. 2020 · 2020
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Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020 · 2020
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Intellicode compose: Code generation using transformer. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1433–1443
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan. 2020 · 2020
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Learning autocompletion from real-world datasets. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) . IEEE, 131–139
Gareth Ari Aye, Seohyun Kim, and Hongyu Li. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Towards automating code review at scale. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1479–1482
Vincent J Hellendoorn, Jason Tsay, Manisha Mukherjee, and Martin Hirzel. 2021 · 2021
Cited alongside, same era.
Cure: Code-aware neural machine translation for automatic program repair. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 1161–1173
Nan Jiang, Thibaud Lutellier, and Lin Tan. 2021a · 2021
Cited alongside, same era.
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
Later among the works it cites.
A systematic literature review on the use of deep learning in software engineering research
Cody Watson, Nathan Cooper, David Nader Palacio, Kevin Moran, and Denys Poshyvanyk. 2022 · 2022
Later among the works it cites.
Awesome Chatgpt Prompts
Fatih Kadir Akın. 2023 · 2023
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ExtraResourceLinkage
ChatGPTCodeReview. 2023 · 2023
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chatgptcodereview-settings
Guo et al. 2023 · 2023
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An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering
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Code prediction by feeding trees to transformers. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 150–162
Seohyun Kim, Jinman Zhao, Yuchi Tian, and Satish Chandra. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Studying the usage of text-to-text transfer transformer to support code-related tasks. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 336–347
Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader Palacio, Denys Poshyvanyk, Rocco Oliveto, and Gabriele Bavota. 2021 · 2021
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 Tufano, Denys Poshyvanyk, and Gabriele Bavota. 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 . Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 8696–8708
Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi. 2021 · 2021
Cited alongside, same era.
UniXcoder: Unified Cross-Modal Pre-training for Code Representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
Cited alongside, same era.
IBM Global AI Adoption Index 2022
IBM. 2022 · 2022
Cited alongside, same era.
CodeReviewer: Pre-Training for Automating Code Review Activities
Zhiyu Li, Shuai Lu, Daya Guo, Nan Duan, Shailesh Jannu, Grant Jenks, Deep Majumder, Jared Green, Alexey Svyatkovskiy, Shengyu Fu, and Neel Sundaresan. 2022 · 2022
Cited alongside, same era.
Nan Hu, Yike Wu, Guilin Qi, Dehai Min, Jiaoyan Chen, Jeff Z Pan, and Zafar Ali. 2023 · 2023
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Multi-target Backdoor Attacks for Code Pre-trained Models
Yanzhou Li, Shangqing Liu, Kangjie Chen, Xiaofei Xie, Tianwei Zhang, and Yang Liu. 2023 · 2023
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ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning
Shangqing Liu, Bozhi Wu, Xiaofei Xie, Guozhu Meng, and Yang Liu. 2023b · 2023
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The Scope of ChatGPT in Software Engineering: A Thorough Investigation
Wei Ma, Shangqing Liu, Wenhan Wang, Qiang Hu, Ye Liu, Cen Zhang, Liming Nie, and Yang Liu. 2023 · 2023
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Is chatgpt a general-purpose natural language processing task solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023 · 2023
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ttest_ind
Scipy. 2023 · 2023
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An Analysis of the Automatic Bug Fixing Performance of ChatGPT
Dominik Sobania, Martin Briesch, Carol Hanna, and Justyna Petke. 2023 · 2023
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Jules White, Sam Hays, Quchen Fu, Jesse Spencer-Smith, and Douglas C. Schmidt. 2023 · 2023
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Generation-based Code Review Automation: How Far Are We?
Xin Zhou, Kisub Kim, Bowen Xu, DongGyun Han, Junda He, and David Lo. 2023 · 2023
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