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Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains.
Simplifying and isolating failure-inducing input
Andreas Zeller and Ralf Hildebrandt. 2002 · 2002
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Learning to Represent Programs with Graphs. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings . OpenReview.net
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Codesearchnet challenge: Evaluating the state of semantic code search
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Pre-trained contextual embedding of source code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi. 2019 · 2019
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Extracting API tips from developer question and answer websites. In Proceedings of the 16th International Conference on Mining Software Repositories (Montreal, Quebec, Canada) (MSR ’19) . IEEE Press, 321–332
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Xlnet: Generalized autoregressive pretraining for language understanding
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PyMT5: multi-mode translation of natural language and Python code with transformers
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020 (Findings of ACL, Vol. EMNLP 2020) , Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, 1536–1547
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Scelmo: Source code embeddings from language models
Rafael-Michael Karampatsis and Charles Sutton. 2020 · 2020
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DLFix: context-based code transformation learning for automated program repair. 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, 602–614
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Suggesting natural method names to check name consistencies. 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, 1372–1384
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Unified pre-training for program understanding and generation
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
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GraphCodeBERT: Pre-training Code Representations with Data Flow. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 . OpenReview.net
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Treebert: A tree-based pre-trained model for programming language. In Uncertainty in Artificial Intelligence . PMLR, 54–63
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What do pre-trained code models know about code?. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 1332–1336
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Multilingual training for software engineering. In Proceedings of the 44th International Conference on Software Engineering (Pittsburgh, Pennsylvania) (ICSE ’22) . Association for Computing Machinery, New York, NY, USA, 1443–1455
Toufique Ahmed and Premkumar Devanbu. 2022 · 2022
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Unixcoder: Unified cross-modal pre-training for code representation
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CodeReviewer: Pre-training for automating code review activities
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The best of both worlds: integrating semantic features with expert features for defect prediction and localization. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (, Singapore, Singapore,) (ESEC/FSE 2022) . Association for Computing Machinery, New York, NY, USA, 672–683
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A Context-based Automated Approach for Method Name Consistency Checking and Suggestion. In Proceedings of the 43rd International Conference on Software Engineering (Madrid, Spain) (ICSE ’21) . IEEE Press, 574–586
Yi Li, Shaohua Wang, and Tien N. Nguyen. 2021a · 2021
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Fault Localization with Code Coverage Representation Learning. In Proceedings of the 43rd International Conference on Software Engineering (Madrid, Spain) (ICSE ’21) . IEEE Press, 661–673
Yi Li, Shaohua Wang, and Tien N. Nguyen. 2021b · 2021
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Vulnerability detection with fine-grained interpretations. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Athens, Greece) (ESEC/FSE 2021) . Association for Computing Machinery, New York, NY, USA, 292–303
Yi Li, Shaohua Wang, and Tien N. Nguyen. 2021c · 2021
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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) . 336–347
Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader Palacio, Denys Poshyvanyk, Rocco Oliveto, and Gabriele Bavota. 2021 · 2021
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Thinking Like a Developer? Comparing the Attention of Humans with Neural Models of Code. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . 867–879
Matteo Paltenghi and Michael Pradel. 2021 · 2021
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Understanding neural code intelligence through program simplification. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Athens, Greece) (ESEC/FSE 2021) . Association for Computing Machinery, New York, NY, USA, 441–452
Md Rafiqul Islam Rabin, Vincent J. Hellendoorn, and Mohammad Amin Alipour. 2021 · 2021
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A primer in BERTology: What we know about how BERT works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2021 · 2021
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Probing model signal-awareness via prediction-preserving input minimization. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Athens, Greece) (ESEC/FSE 2021) . Association for Computing Machinery, New York, NY, USA, 945–955
Sahil Suneja, Yunhui Zheng, Yufan Zhuang, Jim A. Laredo, and Alessandro Morari. 2021 · 2021
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Chao Ni, Wei Wang, Kaiwen Yang, Xin Xia, Kui Liu, and David Lo. 2022a · 2022
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Defect Identification, Categorization, and Repair: Better Together
Chao Ni, Kaiwen Yang, Xin Xia, David Lo, Xiang Chen, and Xiaohu Yang. 2022b · 2022
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CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022 · 2022
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Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022 , Luc De Raedt (Ed.). ijcai.org, 5546–5555
Changan Niu, Chuanyi Li, Bin Luo, and Vincent Ng. 2022 · 2022
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What do they capture? a structural analysis of pre-trained language models for source code. In Proceedings of the 44th International Conference on Software Engineering (Pittsburgh, Pennsylvania) (ICSE ’22) . Association for Computing Machinery, New York, NY, USA, 2377–2388
Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, and Hai Jin. 2022 · 2022
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Diet Code is Healthy: Simplifying Programs for Pre-Trained Models of Code. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Singapore, Singapore) (ESEC/FSE 2022) . Association for Computing Machinery, New York, NY, USA, 1073–1084
Zhaowei Zhang, Hongyu Zhang, Beijun Shen, and Xiaodong Gu. 2022 · 2022
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Latitude
Latitude [n. d.] · 2023
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OpenAI. 2023 · 2023
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DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection. In Proceedings of the 45th International Conference on Software Engineering (Melbourne, Victoria, Australia) (ICSE ’23) . IEEE Press, 2249–2261
Wenbo Wang, Tien N. Nguyen, Shaohua Wang, Yi Li, Jiyuan Zhang, and Aashish Yadavally. 2023b · 2023
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Codet5+: Open code large language models for code understanding and generation
Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi DQ Bui, Junnan Li, and Steven CH Hoi. 2023a · 2023
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