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Developing models that can automatically generate detailed code explanation can greatly benefit software maintenance and programming education.
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Codebert: A pre-trained model for programming and natural languages
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A transformer-based approach for source code summarization
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Supporting program comprehension with source code summarization
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Laura Moreno, Andrian Marcus, Lori Pollock, and K Vijay-Shanker. 2013 · 2013
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Autocomment: Mining question and answer sites for automatic comment generation
Edmund Wong, Jinqiu Yang, and Lin Tan. 2013 · 2013
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Automatic documentation generation via source code summarization of method context
Paul W McBurney and Collin McMillan. 2014 · 2014
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Usage and usefulness of technical software documentation: An industrial case study
Golara Garousi, Vahid Garousi-Yusifoğlu, Guenther Ruhe, Junji Zhi, Mahmoud Moussavi, and Brian Smith. 2015 · 2015
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Accurate evaluation of segment-level machine translation metrics
A survey of automatic generation of source code comments: Algorithms and techniques
Xiaotao Song, Hailong Sun, Xu Wang, and Jiafei Yan. 2019 · 2019
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A large-scale empirical study on code-comment inconsistencies
Fengcai Wen, Csaba Nagy, Gabriele Bavota, and Michele Lanza. 2019 · 2019
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Augmenting java method comments generation with context information based on neural networks
Yu Zhou, Xin Yan, Wenhua Yang, Taolue Chen, and Zhiqiu Huang. 2019 · 2019
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The pile: An 800gb dataset of diverse text for language modeling
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Code to comment “translation”: Data, metrics, baselining & evaluation
David Gros, Hariharan Sezhiyan, Prem Devanbu, and Zhou Yu. 2020 · 2020
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Yvette Graham, Timothy Baldwin, and Nitika Mathur. 2015 · 2015
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Antonio Valerio Miceli Barone and Rico Sennrich. 2017 · 2017
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Deep code comment generation
Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin. 2018 · 2018
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A structured review of the validity of bleu
Ehud Reiter. 2018 · 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 · 2019
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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, 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, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021a
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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. 2021b
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Retrieval-based neural source code summarization
Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, and Xudong Liu. 2020 · 2020
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
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Reassessing automatic evaluation metrics for code summarization tasks
Devjeet Roy, Sarah Fakhoury, and Venera Arnaoudova. 2021 · 2021
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Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021 · 2021
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Practitioners’ expectations on automated code comment generation
Xing Hu, Xin Xia, David Lo, Zhiyuan Wan, Qiuyuan Chen, and Thomas Zimmermann. 2022 · 2022
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