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When debugging unintended program behavior, developers can often identify the point in the execution where the actual behavior diverges from the desired behavior.
Automatically Finding Patches Using Genetic Programming. In International Conference on Software Engineering (ICSE) . 363–374
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Attention is All you Need. In NIPS . 6000–6010
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Boosting spectrum-based fault localization using pagerank. In 26th ACM SIGSOFT International Symposium on Software Testing and Analysis
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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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Deepbugs: A learning approach to name-based bug detection
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Context-aware patch generation for better automated program repair. In 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) . IEEE, 1–11
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SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
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Automated program repair
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Precise learn-to-rank fault localization using dynamic and static features of target programs
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Automated program repair
Claire Le Goues, Michael Pradel, and Abhik Roychoudhury. 2019 · 2019
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Can Automated Program Repair Refine Fault Localization?
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Neural program repair by jointly learning to localize and repair
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Boosting coverage-based fault localization via graph-based representation learning. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 664–676
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Modeling functional similarity in source code with graph-based Siamese networks
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Show Your Work: Scratchpads for Intermediate Computation with Language Models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
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Semantic bug seeding: a learning-based approach for creating realistic bugs. In ESEC/FSE ’21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Athens, Greece, August 23-28, 2021 , Diomidis Spinellis, Georgios Gousios, Marsha Chechik, and Massimiliano Di Penta (Eds.). ACM, 906–918
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A learning-based approach for automatic construction of domain glossary from source code and documentation. In Proceedings of the 2019 27th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering . 97–108
Chong Wang, Xin Peng, Mingwei Liu, Zhenchang Xing, Xuefang Bai, Bing Xie, and Tuo Wang. 2019 · 2019
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Codit: Code editing with tree-based neural models
Saikat Chakraborty, Yangruibo Ding, Miltiadis Allamanis, and Baishakhi Ray. 2020 · 2020
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Patching as translation: the data and the metaphor. In 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 275–286
Yangruibo Ding, Baishakhi Ray, Premkumar Devanbu, and Vincent J Hellendoorn. 2020 · 2020
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Big code != big vocabulary: open-vocabulary models for source code. In ICSE ’20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June - 19 July, 2020 , Gregg Rothermel and Doo-Hwan Bae (Eds.). ACM, 1073–1085
Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, and Andrea Janes. 2020 · 2020
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CoCoNuT: combining context-aware neural translation models using ensemble for program repair. In ISSTA ’20: 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, USA, July 18-22, 2020 , Sarfraz Khurshid and Corina S. Pasareanu (Eds.). ACM, 101–114
Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, and Lin Tan. 2020 · 2020
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Typewriter: Neural type prediction with search-based validation. In 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Michael Pradel, Georgios Gousios, Jason Liu, and Satish Chandra. 2020 · 2020
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Blended, precise semantic program embeddings. In Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation . 121–134
Ke Wang and Zhendong Su. 2020 · 2020
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Jibesh Patra and Michael Pradel. 2021 · 2021
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TREX: Learning Execution Semantics from Micro-Traces for Binary Similarity. In 2021 IEEE Symposium on Security and Privacy
Kexin Pei, Zhou Xuan, Junfeng Yang, Suman Jana, and Baishakhi Ray. 2021b · 2021
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CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual , Joaquin Vanschoren and Sai-Kit Yeung (Eds.)
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DreamLoc: A Deep Relevance Matching-Based Framework for bug Localization
Binhang Qi, Hailong Sun, Wei Yuan, Hongyu Zhang, and Xiangxin Meng. 2021 · 2021
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MergeBERT: Program Merge Conflict Resolution via Neural Transformers
Alexey Svyatkovskiy, Todd Mytkowicz, Negar Ghorbani, Sarah Fakhoury, Elizabeth Dinella, Christian Bird, Neel Sundaresan, and Shuvendu Lahiri. 2021 · 2021
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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 . Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 8696–8708
Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi. 2021 · 2021
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A syntax-guided edit decoder for neural program repair. In ESEC/FSE ’21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Athens, Greece, August 23-28, 2021 , Diomidis Spinellis, Georgios Gousios, Marsha Chechik, and Massimiliano Di Penta (Eds.). ACM, 341–353
Qihao Zhu, Zeyu Sun, Yuan-an Xiao, Wenjie Zhang, Kang Yuan, Yingfei Xiong, and Lu Zhang. 2021a · 2021
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NatGen: Generative pre-training by" Naturalizing" source code. In Proceedings of the 2022 30th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering
Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding, Premkumar Devanbu, and Baishakhi Ray. 2022 · 2022
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VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements. In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 959–970
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On Distribution Shift in Learning-based Bug Detectors. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research, Vol. 162) , Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, and Sivan Sabato (Eds.). PMLR, 8559–8580
Jingxuan He, Luca Beurer-Kellner, and Martin T. Vechev. 2022 · 2022
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Review4Repair: Code review aided automatic program repairing
Faria Huq, Masum Hasan, Md Mahim Anjum Haque, Sazan Mahbub, Anindya Iqbal, and Toufique Ahmed. 2022 · 2022
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SymLM: Predicting Function Names in Stripped Binaries via Context-Sensitive Execution-Aware Code Embeddings. In 2022 ACM SIGSAC Conference on Computer and Communications Security
Xin Jin, Kexin Pei, Jun Yeon Won, and Zhiqiang Lin. 2022 · 2022
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Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
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Generating Realistic Vulnerabilities via Neural Code Editing: An Empirical Study. In ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)
Yu Nong, Yuzhe Ou, Michael Pradel, Feng Chen, and Haipeng Cai. 2022 · 2022
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Nalin: Learning from Runtime Behavior to Find Name-Value Inconsistencies in Jupyter Notebooks. In 2022 IEEE 31st International Conference on Software Engineering (ICSE)
Jibesh Patra and Michael Pradel. 2022 · 2022
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Michael Pradel and Satish Chandra. 2022 · 2022
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On the Evaluation of Neural Code Summarization. In 2022 International Conference on Software Engineering
Ensheng Shia, Yanlin Wangb, Lun Dub, Junjie Chenc, Shi Hanb, Hongyu Zhangd, Dongmei Zhangb, and Hongbin Suna. 2022 · 2022
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Neural Program Repair with Execution-based Backpropagation. In ICSE
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CoditT5: Pretraining for Source Code and Natural Language Editing
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