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With the growing interest on Large Language Models (LLMs) for fault localization and program repair, ensuring the integrity and generalizability of the LLM-based methods becomes paramount.
Sequencer: Sequence-to-sequence learning for end-to-end program repair
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The ManyBugs and IntroClass benchmarks for automated repair of C programs
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INTROCLASS dataset
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Fault localization for automated program repair: effectiveness, performance, repair correctness
Fatmah Yousef Assiri and James M Bieman. 2017 · 2017
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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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Evaluating and improving fault localization. In 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) . IEEE, 609–620
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Compilation error repair: for the student programs, from the student programs. In Proceedings of the 40th International Conference on Software Engineering: Software Engineering Education and Training . 78–87
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An empirical study of fault localization families and their combinations
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Hazards of data leakage in machine learning: a study on classification of breast cancer using deep neural networks. In Medical Imaging 2020: Computer-Aided Diagnosis , Vol. 11314. SPIE, 279–284
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Fatoc: Bug isolation based multi-fault localization by using optics clustering
Yong-Hao Wu, Zheng Li, Yong Liu, and Xiang Chen. 2020 · 2020
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Measuring coding challenge competence with apps
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, et al · 2021
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Extracting concise bug-fixing patches from human-written patches in version control systems. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 686–698
Yanjie Jiang, Hui Liu, Nan Niu, Lu Zhang, and Yamin Hu. 2021 · 2021
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Can we trust the evaluation on ChatGPT?
Rachith Aiyappa, Jisun An, Haewoon Kwak, and Yong-Yeol Ahn. 2023 · 2023
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A study on prompt design, advantages and limitations of chatgpt for deep learning program repair
Jialun Cao, Meiziniu Li, Ming Wen, and Shing-chi Cheung. 2023 · 2023
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Large Language Models for Software Engineering: Survey and Open Problems
Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy, Shubho Sengupta, Shin Yoo, and Jie M Zhang. 2023 · 2023
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Impact of code language models on automated program repair
Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan. 2023 · 2023
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Applying codebert for automated program repair of java simple bugs. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) . IEEE, 505–509
Ehsan Mashhadi and Hadi Hemmati. 2021 · 2021
Cited alongside, same era.
Codenet: A large-scale ai for code dataset for learning a diversity of coding tasks
Ruchir Puri, David S Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, et al · 2021
Cited alongside, same era.
A systematic view of model leakage risks in deep neural network systems
Xing Hu, Ling Liang, Xiaobing Chen, Lei Deng, Yu Ji, Yufei Ding, Zidong Du, Qi Guo, Timothy Sherwood, and Yuan Xie. 2022 · 2022
Cited alongside, same era.
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, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals. 2022 · 2022
Cited alongside, same era.
Fault localization via efficient probabilistic modeling of program semantics. In Proceedings of the 44th International Conference on Software Engineering . 958–969
Muhan Zeng, Yiqian Wu, Zhentao Ye, Yingfei Xiong, Xin Zhang, and Lu Zhang. 2022 · 2022
Cited alongside, same era.
Python-by-contract dataset. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1652–1656
Jiyang Zhang, Marko Ristin, Phillip Schanely, Hans Wernher van de Venn, and Milos Gligoric. 2022 · 2022
Cited alongside, same era.
Sungmin Kang, Gabin An, and Shin Yoo. 2023 · 2023
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An analysis of the automatic bug fixing performance of chatgpt
Dominik Sobania, Martin Briesch, Carol Hanna, and Justyna Petke. 2023 · 2023
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Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair. In Proceedings of the 31th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Yuxiang Wei, Lingming Zhang, and Chunqiu Steven Xia. 2023 · 2023
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Impact of Large Language Models on Generating Software Specifications
Danning Xie, Byungwoo Yoo, Nan Jiang, Mijung Kim, Lin Tan, Xiangyu Zhang, and Judy S Lee. 2023 · 2023
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A Survey of Learning-based Automated Program Repair
Quanjun Zhang, Chunrong Fang, Yuxiang Ma, Weisong Sun, and Zhenyu Chen. 2023 · 2023
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Large Language Models for Test-Free Fault Localization
Aidan ZH Yang, Claire Le Goues, Ruben Martins, and Vincent J Hellendoorn. 2024 · 2024
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