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Recently, contrastive learning has become a key component in fine-tuning code search models for software development efficiency and effectiveness.
Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019 · 1909
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006 · 2006
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Distance metric learning for large margin nearest neighbor classification
Kilian Q Weinberger and Lawrence K Saul. 2009 · 2009
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Improving source code search with natural language phrasal representations of method signatures
Emily Hill, Lori Pollock, and K Vijay-Shanker. 2011 · 2011
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On the sentence embeddings from pre-trained language models
Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, and Lei Li. 2020 · 2011
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Portfolio: finding relevant functions and their usage
Collin McMillan, Mark Grechanik, Denys Poshyvanyk, Qing Xie, and Chen Fu. 2011 · 2011
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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Codehow: Effective code search based on api understanding and extended boolean model (e)
Fei Lv, Hongyu Zhang, Jian-guang Lou, Shaowei Wang, Dongmei Zhang, and Jianjun Zhao. 2015 · 2015
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Query expansion based on crowd knowledge for code search
Liming Nie, He Jiang, Zhilei Ren, Zeyi Sun, and Xiaochen Li. 2016 · 2016
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A search log mining based query expansion technique to improve effectiveness in code search
Abdus Satter and Kazi Sakib. 2016 · 2016
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Déjàvu: a map of code duplicates on github
Cristina V. Lopes, Petr Maj, Pedro Martins, Vaibhav Saini, Di Yang, Jakub Zitny, Hitesh Sajnani, and Jan Vitek. 2017 · 2017
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Combining word2vec with revised vector space model for better code retrieval
Thanh Van Nguyen, Anh Tuan Nguyen, Hung Dang Phan, Trong Duc Nguyen, and Tien N Nguyen. 2017 · 2017
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Iecs: Intent-enforced code search via extended boolean model
Yangrui Yang and Qing Huang. 2017 · 2017
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Deep code search
Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. 2018 · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron Van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
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The adverse effects of code duplication in machine learning models of code
Miltiadis Allamanis. 2019 · 2019
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When deep learning met code search
Jose Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, and Satish Chandra. 2019 · 2019
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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 · 2020
Cited alongside, same era.
Cosqa: 20, 000+ web queries for code search and question answering
Junjie Huang, Duyu Tang, Linjun Shou, Ming Gong, Ke Xu, Daxin Jiang, Ming Zhou, and Nan Duan. 2021 · 2021
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Syncobert: Syntax-guided multi-modal contrastive pre-training for code representation
Xin Wang, Yasheng Wang, Fei Mi, Pingyi Zhou, Yao Wan, Xiao Liu, Li Li, Hao Wu, Jin Liu, and Xin Jiang. 2021 · 2021
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Incremental false negative detection for contrastive learning
Tsai-Shien Chen, Wei-Chih Hung, Hung-Yu Tseng, Shao-Yi Chien, and Ming-Hsuan Yang. 2022 · 2022
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Code search: A survey of techniques for finding code
Luca Di Grazia and Michael Pradel. 2022 · 2022
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Unixcoder: Unified cross-modal pre-training for code representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
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Improving code search with co-attentive representation learning
Jianhang Shuai, Ling Xu, Chao Liu, Meng Yan, Xin Xia, and Yan Lei. 2020 · 2020
Cited alongside, same era.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola. 2020 · 2020
Cited alongside, same era.
Self-supervised contrastive learning for code retrieval and summarization via semantic-preserving transformations
Nghi DQ Bui, Yijun Yu, and Lingxiao Jiang. 2021 · 2021
Cited alongside, same era.
Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Multimodal representation for neural code search
Jian Gu, Zimin Chen, and Martin Monperrus. 2021 · 2021
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
Boosting contrastive self-supervised learning with false negative cancellation
Tri Huynh, Simon Kornblith, Matthew R. Walter, Michael Maire, and Maryam Khademi. 2022 · 2022
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Exploring representation-level augmentation for code search
Haochen Li, Chunyan Miao, Cyril Leung, Yanxian Huang, Yuan Huang, Hongyu Zhang, and Yanlin Wang. 2022a · 2022
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Text and code embeddings by contrastive pre-training
Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, et al. 2022 · 2022
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Cross-modal contrastive learning for code search
Zejian Shi, Yun Xiong, Xiaolong Zhang, Yao Zhang, Shanshan Li, and Yangyong Zhu. 2022c · 2022
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Code search based on context-aware code translation
Weisong Sun, Chunrong Fang, Yuchen Chen, Guanhong Tao, Tingxu Han, and Quanjun Zhang. 2022 · 2022
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Code-mvp: Learning to represent source code from multiple views with contrastive pre-training
Xin Wang, Yasheng Wang, Yao Wan, Jiawei Wang, Pingyi Zhou, Li Li, Hao Wu, and Jin Liu. 2022 · 2022
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Debiased contrastive learning of unsupervised sentence representations
Kun Zhou, Beichen Zhang, Xin Zhao, and Ji-Rong Wen. 2022 · 2022
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