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Automated Program Repair (APR) aims to fix bugs by generating patches.
Sequencer: Sequence-to-sequence learning for end-to-end program repair
Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, and Martin Monperrus. 2019 · 1959
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel. 2022 · 1965
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Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics . 311–318
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Program repair as a game. In Computer Aided Verification: 17th International Conference, CAV 2005, Edinburgh, Scotland, UK, July 6-10, 2005. Proceedings 17 . Springer, 226–238
Barbara Jobstmann, Andreas Griesmayer, and Roderick Bloem. 2005 · 2005
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Genprog: A generic method for automatic software repair
Claire Le Goues, ThanhVu Nguyen, Stephanie Forrest, and Westley Weimer. 2011 · 2011
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Semfix: Program repair via semantic analysis. In 2013 35th International Conference on Software Engineering (ICSE) . IEEE, 772–781
Hoang Duong Thien Nguyen, Dawei Qi, Abhik Roychoudhury, and Satish Chandra. 2013 · 2013
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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
René Just, Darioush Jalali, and Michael D Ernst. 2014 · 2014
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Staged program repair with condition synthesis. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering . 166–178
Fan Long and Martin Rinard. 2015 · 2015
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History driven program repair. In 2016 IEEE 23rd international conference on software analysis, evolution, and reengineering (SANER) , Vol. 1. IEEE, 213–224
Xuan Bach D Le, David Lo, and Claire Le Goues. 2016 · 2016
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Nopol: Automatic repair of conditional statement bugs in java programs
Jifeng Xuan, Matias Martinez, Favio Demarco, Maxime Clement, Sebastian Lamelas Marcote, Thomas Durieux, Daniel Le Berre, and Martin Monperrus. 2016 · 2016
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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
Derrick Lin, James Koppel, Angela Chen, and Armando Solar-Lezama. 2017 · 2017
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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
Jiajun Jiang, Yingfei Xiong, Hongyu Zhang, Qing Gao, and Xiangqun Chen. 2018 · 2018
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Practical program repair via bytecode mutation. In Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis . 19–30
Ali Ghanbari, Samuel Benton, and Lingming Zhang. 2019 · 2019
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Spoc: Search-based pseudocode to code
Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, and Percy S Liang. 2019 · 2019
Earlier work this paper cites.
You cannot fix what you cannot find! an investigation of fault localization bias in benchmarking automated program repair systems. In 2019 12th IEEE conference on software testing, validation and verification (ICST) . IEEE, 102–113
Kui Liu, Anil Koyuncu, Tegawendé F Bissyandé, Dongsun Kim, Jacques Klein, and Yves Le Traon. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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An empirical study on learning bug-fixing patches in the wild via neural machine translation
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. 2019 · 2019
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On the effectiveness of unified debugging: An extensive study on 16 program repair systems. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering . 907–918
Samuel Benton, Xia Li, Yiling Lou, and Lingming Zhang. 2020 · 2020
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Contract-based program repair without the contracts: An extended study
Liushan Chen, Yu Pei, and Carlo A Furia. 2020 · 2020
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On the efficiency of test suite based program repair: A systematic assessment of 16 automated repair systems for java programs. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering . 615–627
Kui Liu, Shangwen Wang, Anil Koyuncu, Kisub Kim, Tegawendé F Bissyandé, Dongsun Kim, Peng Wu, Jacques Klein, Xiaoguang Mao, and Yves Le Traon. 2020 · 2020
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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
Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, and Lin Tan. 2020 · 2020
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Automated patch correctness assessment: How far are we?. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering . 968–980
Shangwen Wang, Ming Wen, Bo Lin, Hongjun Wu, Yihao Qin, Deqing Zou, Xiaoguang Mao, and Hai Jin. 2020 · 2020
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Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations . Association for Computational Linguistics, Online, 38–45
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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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
Earlier work this paper cites.
Beyond tests: Program vulnerability repair via crash constraint extraction
Xiang Gao, Bo Wang, Gregory J Duck, Ruyi Ji, Yingfei Xiong, and Abhik Roychoudhury. 2021 · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
CGenProg: Adaptation of cartesian genetic programming with migration and opposite guesses for automatic repair of software regression faults
Alireza Khalilian, Ahmad Baraani-Dastjerdi, and Bahman Zamani. 2021 · 2021
Cited alongside, same era.
https://openai.com/blog/chatgpt
OpenAI. 2023 · 2023
Later among the works it cites.
Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering
Rishov Paul, Md Mohib Hossain, Mohammed Latif Siddiq, Masum Hasan, Anindya Iqbal, and Joanna CS Santos. 2023 · 2023
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Code Llama: Open Foundation Models for Code
Baptiste Rozière, Jonas Gehring, and Fabian Gloeckle. 2023 · 2023
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Towards efficient fine-tuning of pre-trained code models: An experimental study and beyond. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis . 39–51
Ensheng Shi, Yanlin Wang, Hongyu Zhang, Lun Du, Shi Han, Dongmei Zhang, and Hongbin Sun. 2023 · 2023
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Program repair with minimal edits using codet5. In 2023 12th International Conference on Awareness Science and Technology (iCAST) . IEEE, 178–184
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Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2021 · 2021
Cited alongside, same era.
Codexglue: A machine learning benchmark dataset for code understanding and generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
Cited alongside, same era.
VarFix: balancing edit expressiveness and search effectiveness in automated program repair. In Proceedings of the 29th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering . 354–366
Chu-Pan Wong, Priscila Santiesteban, Christian Kästner, and Claire Le Goues. 2021 · 2021
Cited alongside, same era.
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
Qihao Zhu, Zeyu Sun, Yuan-an Xiao, Wenjie Zhang, Kang Yuan, Yingfei Xiong, and Lu Zhang. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods
Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, Sayak Paul, and Benjamin Bossan. 2022 · 2022
Cited alongside, same era.
Atsushi Shirafuji, Md Mostafizer Rahman, Md Faizul Ibne Amin, and Yutaka Watanobe. 2023 · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Llama 2: Open Foundation and Fine-Tuned Chat Models
Hugo Touvron, Louis Martin, and Kevin Stone. 2023b · 2023
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One adapter for all programming languages? adapter tuning for code search and summarization. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 5–16
Deze Wang, Boxing Chen, Shanshan Li, Wei Luo, Shaoliang Peng, Wei Dong, and Xiangke Liao. 2023a · 2023
Later among the works it cites.
Exploring parameter-efficient fine-tuning techniques for code generation with large language models
Martin Weyssow, Xin Zhou, Kisub Kim, David Lo, and Houari Sahraoui. 2023 · 2023
Later among the works it cites.
Automated program repair in the era of large pre-trained language models. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 1482–1494
Chunqiu Steven Xia, Yuxiang Wei, and Lingming Zhang. 2023 · 2023
Later among the works it cites.
A survey of learning-based automated program repair
Quanjun Zhang, Chunrong Fang, Yuxiang Ma, Weisong Sun, and Zhenyu Chen. 2023 · 2023
Later among the works it cites.
A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
Later among the works it cites.
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Tasks
Wentao Zou, Qi Li, Jidong Ge, Chuanyi Li, Xiaoyu Shen, Liguo Huang, and Bin Luo. 2023 · 2023
Later among the works it cites.
https://github.com/zjulgc/llmpeft4apr
2024 · 2024
Closest in time.
https://huggingface.co
2024 · 2024
Closest in time.
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2024 · 2024
Closest in time.
DeepSeek-Coder: When the Large Language Model Meets Programming – The Rise of Code Intelligence
Daya Guo, Qihao Zhu, and Dejian Yang. 2024 · 2024
Closest in time.
Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. 2024b · 2024
Closest in time.
Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study
Shuo Liu, Jacky Keung, Zhen Yang, Fang Liu, Qilin Zhou, and Yihan Liao. 2024a · 2024
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WizardCoder: Empowering Code Large Language Models with Evol-Instruct. In The Twelfth International Conference on Learning Representations
Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2024 · 2024
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𝔻 2 \mathbb{D}^{2} Pruning: Message Passing for Balancing Diversity & Difficulty in Data Pruning. In The Twelfth International Conference on Learning Representations
Adyasha Maharana, Prateek Yadav, and Mohit Bansal. 2024 · 2024
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Magicoder: Empowering code generation with oss-instruct
Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, and Lingming Zhang. 2024 · 2024
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A Systematic Literature Review on Large Language Models for Automated Program Repair
Quanjun Zhang, Chunrong Fang, Yang Xie, YuXiang Ma, Weisong Sun, and Yun Yang Zhenyu Chen. 2024 · 2024
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