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Code review is an essential part to software development lifecycle since it aims at guaranteeing the quality of codes.
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.
Software inspections and the industrial production of software. In Proc. of a symposium on Software validation: inspection-testing-verification-alternatives . 13–40
A Frank Ackerman, Priscilla J Fowler, and Robert G Ebenau. 1984 · 1984
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.
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.
Impact of Peer Code Review on Peer Impression Formation: A Survey. In ESEM . IEEE Computer Society, 133–142
Amiangshu Bosu and Jeffrey C. Carver. 2013 · 2013
Earlier work this paper cites.
Gerrit software code review data from android. In 2013 10th Working Conference on Mining Software Repositories (MSR) . IEEE, 45–48
Murtuza Mukadam, Christian Bird, and Peter C Rigby. 2013 · 2013
Earlier work this paper cites.
Convergent contemporary software peer review practices. In Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE’13, Saint Petersburg, Russian Federation, August 18-26, 2013 , Bertrand Meyer, Luciano Baresi, and Mira Mezini (Eds.). ACM, 202–212
Peter C. Rigby and Christian Bird. 2013 · 2013
Earlier work this paper cites.
Modern code reviews in open-source projects: which problems do they fix?. In 11th Working Conference on Mining Software Repositories, MSR 2014, Proceedings, May 31 - June 1, 2014, Hyderabad, India , Premkumar T. Devanbu, Sung Kim, and Martin Pinzger (Eds.). ACM, 202–211
Moritz Beller, Alberto Bacchelli, Andy Zaidman, and Elmar Jürgens. 2014 · 2014
Earlier work this paper cites.
Who should review my code? A file location-based code-reviewer recommendation approach for Modern Code Review. In 22nd IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2015, Montreal, QC, Canada, March 2-6, 2015 , Yann-Gaël Guéhéneuc, Bram Adams, and Alexander Serebrenik (Eds.). IEEE Computer Society, 141–150
Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Raula Gaikovina Kula, Norihiro Yoshida, Hajimu Iida, and Ken-ichi Matsumoto. 2015a · 2015
Earlier work this paper cites.
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, Chakkrit Tantithamthavorn, Raula Gaikovina Kula, Norihiro Yoshida, Hajimu Iida, and Ken-ichi Matsumoto. 2015b · 2015
Earlier work this paper cites.
Automatically recommending peer reviewers in modern code review
Motahareh Bahrami Zanjani, Huzefa Kagdi, and Christian Bird. 2015 · 2015
Earlier work this paper cites.
On the Naturalness of Software
Abram Hindle, Earl T. Barr, Mark Gabel, Zhendong Su, and Premkumar Devanbu. 2016 · 2016
Earlier work this paper cites.
Mining the modern code review repositories: a dataset of people, process and product. In Proceedings of the 13th International Conference on Mining Software Repositories, MSR 2016, Austin, TX, USA, May 14-22, 2016 , Miryung Kim, Romain Robbes, and Christian Bird (Eds.). ACM, 460–463
Xin Yang, Raula Gaikovina Kula, Norihiro Yoshida, and Hajimu Iida. 2016a · 2016
Earlier work this paper cites.
Attention is All you Need. In NIPS . 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.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Intelligent code reviews using deep learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’18) Deep Learning Day
Anshul Gupta and Neel Sundaresan. 2018 · 2018
Cited alongside, same era.
CROP: Linking Code Reviews to Source Code Changes. In Proceedings of the 15th International Conference on Mining Software Repositories (Gothenburg, Sweden) (MSR ’18) . Association for Computing Machinery, New York, NY, USA, 46–49
Matheus Paixao, Jens Krinke, Donggyun Han, and Mark Harman. 2018 · 2018
Cited alongside, same era.
Modern code review: a case study at google. In Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice, ICSE (SEIP) 2018, Gothenburg, Sweden, May 27 - June 03, 2018 , Frances Paulisch and Jan Bosch (Eds.). ACM, 181–190
Caitlin Sadowski, Emma Söderberg, Luke Church, Michal Sipko, and Alberto Bacchelli. 2018 · 2018
Cited alongside, same era.
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
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 (Virtual Event, USA) (ESEC/FSE 2020) . Association for Computing Machinery, New York, NY, USA, 1433–1443
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan. 2020 · 2020
Later among the works it 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 . 2655–2668
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Later among the works it 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
Later among the works it cites.
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Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019 · 2019
Cited alongside, same era.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 2019
Cited alongside, same era.
Deepreview: automatic code review using deep multi-instance learning. In Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer, 318–330
Heng-Yi Li, Shu-Ting Shi, Ferdian Thung, Xuan Huo, Bowen Xu, Ming Li, and David Lo. 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.
Automatic code review by learning the revision of source code. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 4910–4917
Shu-Ting Shi, Ming Li, David Lo, Ferdian Thung, and Xuan Huo. 2019 · 2019
Cited alongside, same era.
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. 2019 · 2019
Cited alongside, same era.
WhoReview: A multi-objective search-based approach for code reviewers recommendation in modern code review
Moataz Chouchen, Ali Ouni, Mohamed Wiem Mkaouer, Raula Gaikovina Kula, and Katsuro Inoue. 2021 · 2020
Cited alongside, same era.
Deep learning & software engineering: State of research and future directions
Prem Devanbu, Matthew Dwyer, Sebastian Elbaum, Michael Lowry, Kevin Moran, Denys Poshyvanyk, Baishakhi Ray, Rishabh Singh, and Xiangyu Zhang. 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.
Ting-Rui Chiang, Yi-Pei Chen, Yi-Ting Yeh, and Graham Neubig. 2021 · 2021
Later among the works it cites.
GitHub Copilot · Your AI pair programmer
Github. 2021 · 2021
Later among the works it cites.
GraphCodeBERT: Pre-training Code Representations with Data Flow. In ICLR
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al · 2021
Later among the works it cites.
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 (Athens, Greece) (ESEC/FSE 2021) . Association for Computing Machinery, New York, NY, USA, 1479–1482
Vincent J. Hellendoorn, Jason Tsay, Manisha Mukherjee, and Martin Hirzel. 2021 · 2021
Later among the works it cites.
Exploit Those Code Reviews! Bigger Data for Deeper Learning. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Athens, Greece) (ESEC/FSE 2021) . Association for Computing Machinery, New York, NY, USA, 1505–1509
Robert Heumüller, Sebastian Nielebock, and Frank Ortmeier. 2021 · 2021
Later among the works it cites.
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
Later among the works it cites.
Towards Automating Code Review Activities. In 43rd IEEE/ACM International Conference on Software Engineering, ICSE 2021, Madrid, Spain, 22-30 May 2021 . IEEE, 163–174
Rosalia Tufano, Luca Pascarella, Michele Tufano, Denys Poshyvanyk, and Gabriele Bavota. 2021 · 2021
Later among the works it cites.
Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi. 2021 · 2021
Later among the works it cites.
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Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
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Using Pre-Trained Models to Boost Code Review Automation. In Proceedings of the 44th International Conference on Software Engineering (Pittsburgh, Pennsylvania) (ICSE ’22) . Association for Computing Machinery, New York, NY, USA, 2291–2302
Rosalia Tufano, Simone Masiero, Antonio Mastropaolo, Luca Pascarella, Denys Poshyvanyk, and Gabriele Bavota. 2022 · 2022
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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
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Multilingual Code Snippets Training for Program Translation
Ming Zhu, Karthik Suresh, and Chandan K Reddy. 2022 · 2022
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