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Partial code usually involves non-fully-qualified type names (non-FQNs) and undeclared receiving objects.
Orange: a method for evaluating automatic evaluation metrics for machine translation
Chin-Yew Lin and Franz Josef Och · 2004
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Parseweb: a programmer assistant for reusing open source code on the web
Suresh Thummalapenta and Tao Xie · 2007
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Freebase: a collaboratively created graph database for structuring human knowledge
Kurt D. Bollacker, Colin Evans, Praveen K. Paritosh, Tim Sturge, and Jamie Taylor · 2008
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Enabling static analysis for partial java programs
Barthélémy Dagenais and Laurie Hendren · 2008
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Archetypal internet-scale source code searching
Medha Umarji, Susan Elliott Sim, and Crista Lopes · 2008
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Internet-scale code search
Rosalva Gallardo-Valencia and Susan Sim · 2009
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Two studies of opportunistic programming: interleaving web foraging, learning, and writing code
Joel Brandt, Philip J Guo, Joel Lewenstein, Mira Dontcheva, and Scott R Klemmer · 2009
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Sourcerer: mining and searching internet-scale software repositories
Erik Linstead, Sushil Bajracharya, Trung Ngo, Paul Rigor, Cristina Lopes, and Pierre Baldi · 2009
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Cuebert: A new mixing board concept for musical theatre
Noah Liebman, Michael Nagara, Jacek Spiewla, and Erin Zolkosky · 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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On the naturalness of software
Premkumar T. Devanbu · 2012
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Lexical statistical machine translation for language migration
Anh Tuan Nguyen, Tung Thanh Nguyen, and Tien Nhut Nguyen · 2013
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Live api documentation
Siddharth Subramanian, Laura Inozemtseva, and Reid Holmes · 2014
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Bimodal modelling of source code and natural language
Miltiadis Allamanis, Daniel Tarlow, Andrew D. Gordon, and Yi Wei · 2015
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Challenges with applying vulnerability prediction models
Patrick Morrison, Kim Herzig, Brendan Murphy, and Laurie Williams · 2015
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Kumar Divvala, Ross B. Girshick, and Ali Farhadi · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Z. Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason R. Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Gregory S. Corrado, Macduff Hughes, and Jeffrey Dean · 2016
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Challenges of security and trust of mobile devices as digital avionics component
Raja Naeem Akram and Konstantinos Markantonakis · 2016
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Spoon: A library for implementing analyses and transformations of java source code
Renaud Pawlak, Monperrus Martin, Nicolas Petitprez, Carlos Noguera, and Lionel Seinturier · 2016
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Attention is all you need
Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Are code examples on an online q&a forum reliable?: A study of api misuse on stack overflow
Tianyi Zhang, Ganesha Upadhyaya, Anastasia Reinhardt, Hridesh Rajan, and Miryung Kim · 2018
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A survey of machine learning for big code and naturalness
Miltiadis Allamanis, Earl T. Barr, Premkumar T. Devanbu, and Charles Sutton · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Statistical learning of api fully qualified names in code snippets of online forums
Hung Dang Phan, Hoan Anh Nguyen, Ngoc M. Tran, Linh-Huyen Truong, Anh Tuan Nguyen, and Tien Nhut Nguyen · 2018
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Improving api caveats accessibility by mining api caveats knowledge graph
Hongwei Li, Sirui Li, Jiamou Sun, Zhenchang Xing, Xin Peng, Mingwei Liu, and Xuejiao Zhao · 2018
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Facoy – a code-to-code search engine
Kisub Kim, Dongsun Kim, Tegawendé F. Bissyandé, Eunjong Choi, Li Li, Jacques Klein, and Yves Le Traon · 2018
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Learning from examples to find fully qualified names of api elements in code snippets
C. M. Khaled Saifullah, Muhammad Asaduzzaman, and Chanchal Kumar Roy · 2019
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Yaqin Zhou, Shangqing Liu, J. Siow, Xiaoning Du, and Yang Liu · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel · 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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Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Hongqi Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt · 2019
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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
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Analyzing and supporting adaptation of online code examples
Tianyi Zhang, Di Yang, Crista Lopes, and Miryung Kim · 2019
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Usage and attribution of stack overflow code snippets in github projects
Sebastian Baltes and Stephan Diehl · 2019
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How do developers utilize source code from stack overflow?
Yuhao Wu, Shaowei Wang, Cor-Paul Bezemer, and Katsuro Inoue · 2019
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Know-how in programming tasks: From textual tutorials to task-oriented knowledge graph
Jiamou Sun, Zhenchang Xing, Rui Chu, Heilai Bai, Jinshui Wang, and Xin Peng · 2019
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Generating query-specific class api summaries
Dcom: A deep column mapper for semantic data type detection
Subhadip Maji, Swapna Sourav Rout, and Sudeep Choudhary · 2021
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Crylogger: Detecting crypto misuses dynamically
Luca Piccolboni, Giuseppe Di Guglielmo, Luca P. Carloni, and Simha Sethumadhavan · 2021
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Yi Sun, Yu Zheng, Chao Hao, and Hangping Qiu · 2021
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Ptr: Prompt tuning with rules for text classification
Xu Han, Weilin Zhao, Ning Ding, Zhiyuan Liu, and Maosong Sun · 2021
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Mingwei Liu, Xin Peng, Andrian Marcus, Zhenchang Xing, Wenkai Xie, Shuangshuang Xing, and Yang Liu · 2019
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Enhance code search via reformulating queries with evolving contexts
Qing Huang and Guoqing Wu · 2019
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Qe-integrating framework based on github knowledge and svm ranking
Qing Huang and Huaiguang Wu · 2019
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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
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Improving bert fine-tuning with embedding normalization
Wenxuan Zhou, Junyi Du, and Xiang Ren · 2019
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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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Jcoffee: Using compiler feedback to make partial code snippets compilable
Piyush Kumar Gupta, Nikita Mehrotra, and Rahul Purandare · 2020
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Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang · 2021
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Prompt-learning for fine-grained entity typing
Ning Ding, Yulin Chen, Xu Han, Guangwei Xu, Pengjun Xie, Haitao Zheng, Zhiyuan Liu, Juan-Zi Li, and Hong-Gee Kim · 2021
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P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, and Jie Tang · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze · 2021
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Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze · 2021
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What do pre-trained code models know about code?
Anjan Karmakar and Romain Robbes · 2021
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Codexglue: A machine learning benchmark dataset for code understanding and generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin B. Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and Shujie Liu · 2021
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Bridging pre-trained models and downstream tasks for source code understanding
Deze Wang, Zhouyang Jia, Shanshan Li, Yue Yu, Yun Xiong, Wei Dong, and Xiangke Liao · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2021
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An empirical cybersecurity evaluation of github copilot’s code contributions
Hammond A. Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri · 2021
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll’ar, and Ross B. Girshick · 2021
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Code completion by modeling flattened abstract syntax trees as graphs
Yanlin Wang and Hui Li · 2021
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Benjamin Heinzerling and Kentaro Inui · 2021
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Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi · 2021
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A new search paradigm for natural language code search
Anonymous · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Snr: Constraint based type inference for incomplete java code snippets
Yiwen Dong, Tianxiao Gu, Yongqiang Tian, and Chengnian Sun · 2022
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Analyzing codebert’s performance on natural language code search
Anonymous · 2022
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Probing pretrained models of source code
Sergey Troshin and Nadezhda Chirkova · 2022
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What do they capture? - a structural analysis of pre-trained language models for source code
Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, and Hairong Jin · 2022
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Assemble foundation models for automatic code summarization
Jian Gu, Pasquale Salza, and Harald C. Gall · 2022
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What do they capture?–a structural analysis of pre-trained language models for source code
Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, and Hai Jin · 2022
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Probing pretrained models of source code
Sergey Troshin and Nadezhda Chirkova · 2022
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Context-tuning: Learning contextualized prompts for natural language generation
Tianyi Tang, Junyi Li, and Wayne Xin Zhao · 2022
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