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(Source) code summarization is the task of automatically generating natural language summaries (also called comments) for given code snippets.
Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test
Frank Wilcoxon, SK Katti, and Roberta A Wilcox · 1963
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Program readability: Procedures versus comments
Ted Tenny · 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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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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A study of the documentation essential to software maintenance
Sergio Cozzetti B. de Souza, Nicolas Anquetil, and Káthia Marçal de Oliveira · 2005
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METEOR: an automatic metric for MT evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie · 2005
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Supporting program comprehension with source code summarization
Sonia Haiduc, Jairo Aponte, and Andrian Marcus · 2010
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Towards automatically generating summary comments for java methods
Giriprasad Sridhara, Emily Hill, Divya Muppaneni, Lori L. Pollock, and K. Vijay-Shanker · 2010
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On the use of automated text summarization techniques for summarizing source code
Sonia Haiduc, Jairo Aponte, Laura Moreno, and Andrian Marcus · 2010
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Cliff’s delta calculator: A non-parametric effect size program for two groups of observations
Guillermo Macbeth, Eugenia Razumiejczyk, and Rubén Daniel Ledesma · 2011
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Evaluating source code summarization techniques: Replication and expansion
Brian P. Eddy, Jeffrey A. Robinson, Nicholas A. Kraft, and Jeffrey C. Carver · 2013
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Automatic generation of natural language summaries for java classes
Laura Moreno, Jairo Aponte, Giriprasad Sridhara, Andrian Marcus, Lori L. Pollock, and K. Vijay-Shanker · 2013
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On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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On automatically generating commit messages via summarization of source code changes
Luis Fernando Cortes-Coy, Mario Linares Vásquez, Jairo Aponte, and Denys Poshyvanyk · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Summarizing source code using a neural attention model
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, undefinedukasz Kaiser, and Illia Polosukhin · 2017
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Summarizing source code with transferred API knowledge
Xing Hu, Ge Li, Xin Xia, David Lo, Shuai Lu, and Zhi Jin · 2018
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Deep code comment generation
Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin · 2018
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Improving automatic source code summarization via deep reinforcement learning
Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, and Philip S. Yu · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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A convolutional neural network for language-agnostic source code summarization
Jessica Moore, Ben Gelman, and David Slater · 2019
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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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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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A human study of comprehension and code summarization
Sean Stapleton, Yashmeet Gambhir, Alexander LeClair, Zachary Eberhart, Westley Weimer, Kevin Leach, and Yu Huang · 2020
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Retrieval-based neural source code summarization
Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, and Xudong Liu · 2020
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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
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Deep code comment generation with hybrid lexical and syntactical information
Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin · 2020
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Code to comment ”translation”: Data, metrics, baselining & evaluation
David Gros, Hariharan Sezhiyan, Prem Devanbu, and Zhou Yu · 2020
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A transformer-based approach for source code summarization
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 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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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, and Ming Zhou · 2020
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Retrieve and refine: Exemplar-based neural comment generation
Bolin Wei, Yongmin Li, Ge Li, Xin Xia, and Zhi Jin · 2020
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Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 2022
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An extractive-and-abstractive framework for source code summarization
Weisong Sun, Chunrong Fang, Yuchen Chen, Quanjun Zhang, Guanhong Tao, Yudu You, Tingxu Han, Yifei Ge, Yuling Hu, Bin Luo, and Zhenyu Chen · 2023
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Automatic code summarization via chatgpt: How far are we?
Weisong Sun, Chunrong Fang, Yudu You, Yun Miao, Yi Liu, Yuekang Li, Gelei Deng, Shenghan Huang, Yuchen Chen, Quanjun Zhang, Hanwei Qian, Yang Liu, and Zhenyu Chen · 2023
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Function call graph context encoding for neural source code summarization
Aakash Bansal, Zachary Eberhart, Zachary Karas, Yu Huang, and Collin McMillan · 2023
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One adapter for all programming languages? adapter tuning for code search and summarization
Deze Wang, Boxing Chen, Shanshan Li, Wei Luo, Shaoliang Peng, Wei Dong, and Xiangke Liao · 2023
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MAD-X: an adapter-based framework for multi-task cross-lingual transfer
Jonas Pfeiffer, Ivan Vulic, Iryna Gurevych, and Sebastian Ruder · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, et al · 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
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NASOA: towards faster task-oriented online fine-tuning with a zoo of models
Hang Xu, Ning Kang, Gengwei Zhang, Chuanlong Xie, Xiaodan Liang, and Zhenguo Li · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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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, Michele Tufano, Shao Kun Deng, Colin B. Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou · 2021
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Unified pre-training for program understanding and generation
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
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Is chatgpt the ultimate programming assistant–how far is it?
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Is chatgpt a general-purpose natural language processing task solver?
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Large language models for software engineering: Survey and open problems
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Large language models for software engineering: A systematic literature review
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Starcoder: may the source be with you!
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Exploring parameter-efficient fine-tuning techniques for code generation with large language models
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