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Developers deal with code-change-related tasks daily, e.g., reviewing code.
Classifying software maintenance
I-H Lin and David A Gustafson · 1988
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Statistical significance tests for machine translation evaluation
Philipp Koehn · 2004
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Change distilling: Tree differencing for fine-grained source code change extraction
Beat Fluri, Michael Würsch, Martin Pinzger, and Harald C. Gall · 2007
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Automatically documenting program changes
Raymond PL Buse and Westley R Weimer · 2010
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Using information fragments to answer the questions developers ask
Thomas Fritz and Gail C Murphy · 2010
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Design and code inspections to reduce errors in program development
Michael Fagan · 2011
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Impact of peer code review on peer impression formation: A survey
Amiangshu Bosu and Jeffrey C Carver · 2013
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Hey! are you committing tangled changes?
Hiroyuki Kirinuki, Yoshiki Higo, Keisuke Hotta, and Shinji Kusumoto · 2014
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Studying just-in-time defect prediction using cross-project models
Yasutaka Kamei, Takafumi Fukushima, Shane McIntosh, Kazuhiro Yamashita, Naoyasu Ubayashi, and Ahmed E Hassan · 2016
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Chia-Wei Liu, Ryan Lowe, Iulian V Serban, Michael Noseworthy, Laurent Charlin, and Joelle Pineau · 2016
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Correct: code reviewer recommendation in github based on cross-project and technology experience
Mohammad Masudur Rahman, Chanchal K Roy, and Jason A Collins · 2016
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Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification
Sanjay Yadav and Sanyam Shukla · 2016
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Automatically generating commit messages from diffs using neural machine translation
Siyuan Jiang, Ameer Armaly, and Collin McMillan · 2017
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A neural architecture for generating natural language descriptions from source code changes
Pablo Loyola, Edison Marrese-Taylor, and Yutaka Matsuo · 2017
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Code review for newcomers: is it different?
Vladimir Kovalenko and Alberto Bacchelli · 2018
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Neural-machine-translation-based commit message generation: how far are we?
Zhongxin Liu, Xin Xia, Ahmed E. Hassan, David Lo, Zhenchang Xing, and Xinyu Wang · 2018
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Model evaluation, model selection, and algorithm selection in machine learning
Sebastian Raschka · 2018
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Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt · 2019
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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 · 2020
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Refactoring operations grounded in manual code changes
Anna Maria Eilertsen · 2020
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Cc2vec: Distributed representations of code changes
Thong Hoang, Hong Jin Kang, David Lo, and Julia Lawall · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Atom: Commit message generation based on abstract syntax tree and hybrid ranking
Shangqing Liu, Cuiyun Gao, Sen Chen, Lun Yiu Nie, and Yang Liu · 2020
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Automating just-in-time comment updating
Zhongxin Liu, Xin Xia, Meng Yan, and Shanping Li · 2020
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Automating just-in-time comment updating
Zhongxin Liu, Xin Xia, Meng Yan, and Shanping Li · 2020
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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
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Core: Automating review recommendation for code changes
Jing Kai Siow, Cuiyun Gao, Lingling Fan, Sen Chen, and Yang Liu · 2020
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Automated comment update: How far are we?
Bo Lin, Shangwen Wang, Kui Liu, Xiaoguang Mao, and Tegawendé F. Bissyandé · 2021
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Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi · 2021
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Deep just-in-time inconsistency detection between comments and source code
Sheena Panthaplackel, Li, et al · 2021
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Scale efficiently: Insights from pre-training and fine-tuning transformers
Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, and Donald Metzler · 2021
Cited alongside, same era.
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al · 2023
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Incoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, et al · 2023
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Constructing effective in-context demonstration for code intelligence tasks: An empirical study
Shuzheng Gao, Xin-Cheng Wen, Cuiyun Gao, Wenxuan Wang, and Michael R Lyu · 2023
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Exploring the potential of chatgpt in automated code refinement: An empirical study
Qi Guo, Junming Cao, Xiaofei Xie, Shangqing Liu, Xiaohong Li, Bihuan Chen, and Xin Peng · 2023
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki · 2021
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Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi · 2021
Cited alongside, same era.
Parameter-efficient finetuning of transformers for source code
Shamil Ayupov and Nadezhda Chirkova · 2022
Cited alongside, same era.
Revisiting parameter-efficient tuning: Are we really there yet?
Guanzheng Chen, Fangyu Liu, Zaiqiao Meng, and Shangsong Liang · 2022
Cited alongside, same era.
Inducer-tuning: Connecting prefix-tuning and adapter-tuning
Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, and Dilek Hakkani-Tur · 2022
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Fira: fine-grained graph-based code change representation for automated commit message generation
Jinhao Dong, Yiling Lou, Qihao Zhu, Zeyu Sun, Zhilin Li, Wenjie Zhang, and Dan Hao · 2022
Cited alongside, same era.
Deep just-in-time consistent comment update via source code changes
Shikai Guo, Xihui Xu, Hui Li, and Rong Chen · 2022
Cited alongside, same era.
Jia Li, Yunfei Zhao, Yongmin Li, Ge Li, and Zhi Jin · 2023
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Starcoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al · 2023
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CCT5: A code-change-oriented pre-trained model
Bo Lin, Shangwen Wang, Zhongxin Liu, Yepang Liu, Xin Xia, and Xiaoguang Mao · 2023
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Radiology-gpt: A large language model for radiology
Zhengliang Liu, Aoxiao Zhong, Yiwei Li, et al · 2023
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Just-in-time obsolete comment detection and update
Zhongxin Liu, Xin Xia, David Lo, Meng Yan, and Shanping Li · 2023
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Llama-reviewer: Advancing code review automation with large language models through parameter-efficient fine-tuning
Junyi Lu, Lei Yu, Xiaojia Li, Li Yang, and Chun Zuo · 2023
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Wizardcoder: Empowering code large language models with evol-instruct
Ziyang Luo, Can Xu, Pu Zhao, et al · 2023
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Using in-context learning to improve dialogue safety
Nicholas Meade, Spandana Gella, et al · 2023
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Crosslingual generalization through multitask finetuning
Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al · 2023
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Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2023
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Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao · 2023
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Automatic prompt optimization with "gradient descent" and beam search
Reid Pryzant, Dan Iter, et al · 2023
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Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, et al · 2023
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Scaling laws vs model architectures: How does inductive bias influence scaling?
Yi Tay, Mostafa Dehghani, Samira Abnar, Hyung Won Chung, William Fedus, Jinfeng Rao, Sharan Narang, Vinh Q. Tran, Dani Yogatama, and Donald Metzler · 2023
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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, et al · 2023
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Automating code review
Rosalia Tufano · 2023
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Exploring parameter-efficient fine-tuning techniques for code generation with large language models
Martin Weyssow, Xin Zhou, Kisub Kim, David Lo, and Houari Sahraoui · 2023
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Qilin-med: Multi-stage knowledge injection advanced medical large language model
Qichen Ye, Junling Liu, Dading Chong, Peilin Zhou, Yining Hua, and Andrew Liu · 2023
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Evaluating instruction-tuned large language models on code comprehension and generation
Zhiqiang Yuan, Junwei Liu, Qiancheng Zi, et al · 2023
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Large language models meet NL2Code: A survey
Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, and Jian-Guang Lou · 2023
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Ccbert: Self-supervised code change representation learning
X. Zhou, B. Xu, D. Han, Z. Yang, J. He, and D. Lo · 2023
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Large language models are few-shot summarizers: Multi-intent comment generation via in-context learning
Mingyang Geng, Shangwen Wang, Dezun Dong, Haotian Wang, Ge Li, Zhi Jin, Xiaoguang Mao, and Xiangke Liao · 2024
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Uncovering the causes of emotions in software developer communication using zero-shot llms
Mia Mohammad Imran, Preetha Chatterjee, and Kostadin Damevski · 2024
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Non-autoregressive line-level code completion
Fang Liu, Zhiyi Fu, Ge Li, Zhi Jin, Hui Liu, Yiyang Hao, and Li Zhang · 2024
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Delving into parameter-efficient fine-tuning in code change learning: An empirical study
Shuo Liu, Jacky Keung, Zhen Yang, Fang Liu, Qilin Zhou, and Yihan Liao · 2024
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