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Recently, pre-trained programming language models such as CodeBERT have demonstrated substantial gains in code search.
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 · 1907
Earlier work this paper cites.
On "A framework for source code search using program patterns"
Premkumar T. Devanbu. 1995 · 1995
Earlier work this paper cites.
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 · 2005
Earlier work this paper cites.
Learning a similarity metric discriminatively, with application to face verification. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) , Vol. 1. IEEE, 539–546
Sumit Chopra, Raia Hadsell, and Yann LeCun. 2005 · 2005
Earlier work this paper cites.
Sourcerer: a search engine for open source code supporting structure-based search. In Companion to the 21st ACM SIGPLAN symposium on Object-oriented programming systems, languages, and applications . 681–682
Sushil Bajracharya, Trung Ngo, Erik Linstead, Yimeng Dou, Paul Rigor, Pierre Baldi, and Cristina Lopes. 2006 · 2006
Earlier work this paper cites.
Meta-learning for Few-shot Natural Language Processing: A Survey
Wenpeng Yin. 2020 · 2007
Earlier work this paper cites.
SWIM: A semantic wiki for mathematical knowledge management
Christoph Lange and Michael Kohlhase. 2008 · 2008
Earlier work this paper cites.
Thesaurus-based automatic query expansion for interface-driven code search. In Proceedings of the 11th working conference on mining software repositories . 212–221
Otávio AL Lemos, Adriano C de Paula, Felipe C Zanichelli, and Cristina V Lopes. 2014 · 2014
Earlier work this paper cites.
Learning to rank relevant files for bug reports using domain knowledge. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering . 689–699
Xin Ye, Razvan Bunescu, and Chang Liu. 2014 · 2014
Earlier work this paper cites.
Query expansion via wordnet for effective code search. In 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER) . IEEE, 545–549
Meili Lu, Xiaobing Sun, Shaowei Wang, David Lo, and Yucong Duan. 2015 · 2015
Earlier work this paper cites.
CodeHow: Effective Code Search Based on API Understanding and Extended Boolean Model (E). In 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) . 260–270
Fei Lv, Hongyu Zhang, Jian-Guang Lou, Shaowei Wang, Dongmei Zhang, and Jianjun Zhao. 2015 · 2015
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning . PMLR, 1126–1135
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Easy over hard: A case study on deep learning. In Proceedings of the 2017 11th joint meeting on foundations of software engineering . 49–60
Wei Fu and Tim Menzies. 2017 · 2017
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba. 2017 · 2017
Earlier work this paper cites.
Prototypical Networks for Few-shot Learning
Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017 · 2017
Earlier work this paper cites.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Studying the Difference Between Natural and Programming Language Corpora
Casey Casalnuovo, Kenji Sagae, and Prem Devanbu. 2018 · 2018
Cited alongside, same era.
Meta-learning for low-resource neural machine translation
Jiatao Gu, Yong Wang, Yun Chen, Kyunghyun Cho, and Victor OK Li. 2018a · 2018
Cited alongside, same era.
Deep code search. In 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) . IEEE, 933–944
Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. 2018b · 2018
Cited alongside, same era.
Meta-transfer learning for few-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 403–412
Qianru Sun, Yaoyao Liu, Tat-Seng Chua, and Bernt Schiele. 2019 · 2019
Later among the works it cites.
BERT has a mouth, and it must speak: BERT as a markov random field language model
Alex Wang and Kyunghyun Cho. 2019 · 2019
Later among the works it cites.
Coacor: Code annotation for code retrieval with reinforcement learning. In The World Wide Web Conference . 2203–2214
Ziyu Yao, Jayavardhan Reddy Peddamail, and Huan Sun. 2019 · 2019
Later among the works it cites.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings . 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
Later among the works it cites.
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Alex Nichol, Joshua Achiam, and John Schulman. 2018 · 2018
Cited alongside, same era.
Retrieval on source code: a neural code search. In Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages . 31–41
Saksham Sachdev, Hongyu Li, Sifei Luan, Seohyun Kim, Koushik Sen, and Satish Chandra. 2018 · 2018
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning. In Proceedings of the IEEE conference on computer vision and pattern recognition . 1199–1208
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales. 2018 · 2018
Cited alongside, same era.
Smart contracts: security patterns in the ethereum ecosystem and solidity. In International Workshop on Blockchain Oriented Software Engineering (IWBOSE) . IEEE, 2–8
Maximilian Wohrer and Uwe Zdun. 2018 · 2018
Cited alongside, same era.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev. 2018 · 2018
Cited alongside, same era.
Towards verification of Ethereum smart contracts: a formalization of core of Solidity. In Working Conference on Verified Software: Theories, Tools, and Experiments . Springer, 229–247
Jakub Zakrzewski. 2018 · 2018
Cited alongside, same era.
When deep learning met code search. In Proceedings of the 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 964–974
Jose Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, and Satish Chandra. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Learning Code-Query Interaction for Enhancing Code Searches. In IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 115–126
Wei Li, Haozhe Qin, Shuhan Yan, Beijun Shen, and Yuting Chen. 2020 · 2020
Later among the works it cites.
Simplifying Deep-Learning-Based Model for Code Search
Chao Liu, Xin Xia, David Lo, Zhiwei Liu, Ahmed E Hassan, and Shanping Li. 2020 · 2020
Later among the works it cites.
Are the Code Snippets What We Are Searching for? A Benchmark and an Empirical Study on Code Search with Natural-Language Queries. In 27th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 344–354
Shuhan Yan, Hang Yu, Yuting Chen, Beijun Shen, and Lingxiao Jiang. 2020 · 2020
Later among the works it cites.
OCoR: an overlapping-aware code retriever. In 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 883–894
Qihao Zhu, Zeyu Sun, Xiran Liang, Yingfei Xiong, and Lu Zhang. 2020 · 2020
Later among the works it cites.
Unified Pre-training for Program Understanding and Generation
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Later among the works it cites.
Studying the usage of text-to-text transfer transformer to support code-related tasks. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 336–347
Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader Palacio, Denys Poshyvanyk, Rocco Oliveto, and Gabriele Bavota. 2021 · 2021
Later among the works it cites.
CoTexT: Multi-task Learning with Code-Text Transformer
Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, and Yanfang Ye. 2021 · 2021
Later among the works it cites.
On the Effectiveness of Transfer Learning for Code Search
Pasquale Salza, Christoph Schwizer, Jian Gu, and Harald C Gall. 2021 · 2021
Later among the works it cites.
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . 8696–8708
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021 · 2021
Later among the works it cites.
A Multi-Modal Transformer-based Code Summarization Approach for Smart Contracts
Zhen Yang, Jacky Keung, Xiao Yu, Xiaodong Gu, Zhengyuan Wei, Xiaoxue Ma, and Miao Zhang. 2021 · 2021
Later among the works it cites.
Enriching query semantics for code search with reinforcement learning
Chaozheng Wang, Zhenhao Nong, Cuiyun Gao, Zongjie Li, Jichuan Zeng, Zhenchang Xing, and Yang Liu. 2022 · 2022
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