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Software development life cycle is profoundly influenced by bugs: their introduction, identification, and eventual resolution account for a significant portion of software cost.
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, and Monperrus Martin. 2021a · 1959
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Defects4J: A database of existing faults to enable controlled testing studies for Java programs. In Proceedings of the 2014 international symposium on software testing and analysis . 437–440
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QuixBugs: A multi-lingual program repair benchmark set based on the Quixey Challenge. In Proceedings Companion of the 2017 ACM SIGPLAN international conference on systems, programming, languages, and applications: software for humanity . 55–56
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A public unified bug dataset for java. In Proceedings of the 14th international conference on predictive models and data analytics in software engineering . 12–21
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Shaping program repair space with existing patches and similar code. In Proceedings of the 27th ACM SIGSOFT international symposium on software testing and analysis . 298–309
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Automatic Software Repair: A Bibliography
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Representation Learning with Contrastive Predictive Coding
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Unsupervised Feature Learning via Non-parametric Instance Discrimination
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Scaling Static Analyses at Facebook
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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) . Association for Computational Linguistics, Hong Kong, China, 2463–2473
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An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
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Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 1877–1901
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Codit: Code editing with tree-based neural models
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How often do single-statement bugs occur? the manysstubs4j dataset. In Proceedings of the 17th International Conference on Mining Software Repositories . 573–577
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Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, Online, 6769–6781
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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 . 602–614
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Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions
Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri. 2021 · 2021
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A syntax-guided edit decoder for neural program repair. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 341–353
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PaLM: Scaling Language Modeling with Pathways
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Coconut: combining context-aware neural translation models using ensemble for program repair. In Proceedings of the 29th ACM SIGSOFT international symposium on software testing and analysis . 101–114
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Self-Supervised Bug Detection and Repair. In Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 27865–27876
Miltiadis Allamanis, Henry Jackson-Flux, and Marc Brockschmidt. 2021 · 2021
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Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 4713–4722
Colin Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, and Alexey Svyatkovskiy. 2021 · 2021
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Template-Based Named Entity Recognition Using BART. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 . Association for Computational Linguistics, Online, 1835–1845
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Generating Bug-Fixes Using Pretrained Transformers. In Proceedings of the 5th ACM SIGPLAN International Symposium on Machine Programming (Virtual, Canada) (MAPS 2021) . Association for Computing Machinery, New York, NY, USA, 1–8
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Cure: Code-aware neural machine translation for automatic program repair. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 1161–1173
Nan Jiang, Thibaud Lutellier, and Lin Tan. 2021 · 2021
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Copy that! Editing Sequences by Copying Spans. In AAAI
Sheena Panthaplackel, Miltiadis Allamanis, and Marc Brockschmidt. 2021 · 2021
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InCoder: A Generative Model for Code Infilling and Synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis. 2022 · 2022
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Repair Is Nearly Generation: Multilingual Program Repair with LLMs
Harshit Joshi, José Cambronero, Sumit Gulwani, Vu Le, Ivan Radicek, and Gust Verbruggen. 2022 · 2022
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Pre-Train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2022 · 2022
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ReACC: A Retrieval-Augmented Code Completion Framework. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Dublin, Ireland, 6227–6240
Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, and Alexey Svyatkovskiy. 2022 · 2022
Later among the works it cites.
Chain of Thought Prompting Elicits Reasoning in Large Language Models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
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acs-aem-common
Adobe-Consulting-Services. 2023 · 2023
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Pre-Train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
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Microsoft. 2023 · 2023
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