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Many recent models in software engineering introduced deep neural models based on the Transformer architecture or use transformer-based Pre-trained Language Models (PLM) trained on code.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
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Attention Is All You Need. In Advances in Neural Information Processing Systems
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Automated software vulnerability detection with machine learning
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Deep Code Comment Generation. In Proceedings of the 26th Conference on Program Comprehension (Gothenburg, Sweden) (ICPC ’18) . Association for Computing Machinery, New York, NY, USA, 200–210
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DeepBugs: A Learning Approach to Name-Based Bug Detection
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Descriptive Compound Identifier Names Improve Source Code Comprehension. In 2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC) . 31–3109
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Improving Automatic Source Code Summarization via Deep Reinforcement Learning. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (Montpellier, France) (ASE 2018) . Association for Computing Machinery, New York, NY, USA, 397–407
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code2seq: Generating Sequences from Structured Representations of Code. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019
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Studying the Difference Between Natural and Programming Language Corpora
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What Does BERT Look At? An Analysis of BERT’s Attention. In Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186
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A Neural Model for Generating Natural Language Summaries of Program Subroutines. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)
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Multi-task Learning based Pre-trained Language Model for Code Completion. In 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) . 473–485
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Emergent linguistic structure in artificial neural networks trained by self-supervision
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On the generation, structure, and semantics of grammar patterns in source code identifiers
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Improving Code Search with Co-Attentive Representation Learning
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HuggingFace’s Transformers: State-of-the-art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
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Cited alongside, same era.
A Neural Model for Generating Natural Language Summaries of Program Subroutines. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)
Alexander LeClair, Siyuan Jiang, and Collin McMillan. 2019b · 2019
Cited alongside, same era.
Improving Bug Detection via Context-Based Code Representation Learning and Attention-Based Neural Networks
Yi Li, Shaohua Wang, Tien N. Nguyen, and Son Van Nguyen. 2019 · 2019
Cited alongside, same era.
NL2Type: Inferring JavaScript Function Types from Natural Language Information. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . 304–315
Rabee S. Malik, Jibesh Patra, and Michael Pradel. 2019 · 2019
Cited alongside, same era.
Language Models as Knowledge Bases?. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H. Miller, and Sebastian Riedel. 2019 · 2019
Cited alongside, same era.
A Multiscale Visualization of Attention in the Transformer Model. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations . Association for Computational Linguistics, Florence, Italy, 37–42
Jesse Vig. 2019 · 2019
Cited alongside, same era.
Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities. In 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER) . 479–490
Martin White, Michele Tufano, Matías Martínez, Martin Monperrus, and Denys Poshyvanyk. 2019 · 2019
Cited alongside, same era.
A Transformer-based Approach for Source Code Summarization. In ACL . 4998–5007
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2020 · 2020
Cited alongside, same era.
Embedding Java Classes with code2vec
Rhys Compton, Eibe Frank, Panos Patros, and Abigail Koay. 2020 · 2020
Cited alongside, same era.
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
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Retrieval-Based Neural Source Code Summarization. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (Seoul, South Korea) (ICSE ’20) . Association for Computing Machinery, New York, NY, USA, 1385–1397
Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, and Xudong Liu. 2020 · 2020
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In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
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On the Naming of Methods: A Survey of Professional Developers. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . 587–599
Reem S. Alsuhaibani, Christian D. Newman, Michael J. Decker, Michael L. Collard, and Jonathan I. Maletic. 2021 · 2021
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Assessing Robustness of ML-Based Program Analysis Tools using Metamorphic Program Transformations. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 1377–1381
Leonhard Applis, Annibale Panichella, and Arie van Deursen. 2021 · 2021
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Code Structure Guided Transformer for Source Code Summarization
Shuzheng Gao, Cuiyun Gao, Yulan He, Jichuan Zeng, Lun Yiu Nie, and Xin Xia. 2021 · 2021
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GraphCodeBERT: Pre-training Code Representations with Data Flow. In International Conference on Learning Representations
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 Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. 2021 · 2021
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What do pre-trained code models know about code?
Anjan Karmakar and Romain Robbes. 2021 · 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 · 2021
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On the generalizability of Neural Program Models with respect to semantic-preserving program transformations
Md Rafiqul Islam Rabin, Nghi DQ Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, and Mohammad Amin Alipour. 2021 · 2021
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Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Koustuv Sinha, Robin Jia, Dieuwke Hupkes, Joelle Pineau, Adina Williams, and Douwe Kiela. 2021 · 2021
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Natural Attack for Pre-trained Models of Code. In 2022 IEEE/ACM 41st International Conference on Software Engineering (ICSE)
Zhou Yang, Jieke Shi, Junda He, and David Lo. 2022 · 2022
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