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Automated Program Repair (APR) attempts to patch software bugs and reduce manual debugging efforts.
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Automatically Finding Patches Using Genetic Programming. In 2009 IEEE 31st International Conference on Software Engineering . IEEE, 364–374
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GenProg: A Generic Method for Automatic Software Repair
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Automatically Generated Patches As Debugging Aids: A Human Study. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering . 64–74
Yida Tao, Jindae Kim, Sunghun Kim, and Chang Xu. 2014 · 2014
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Guidelines for Conducting Systematic Mapping Studies in Software Engineering: An Update
Kai Petersen, Sairam Vakkalanka, and Ludwik Kuzniarz. 2015 · 2015
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DynaMoth: Dynamic Code Synthesis for Automatic Program Repair. In Proceedings of the 11th International Workshop on Automation of Software Test . 85–91
Thomas Durieux and Martin Monperrus. 2016 · 2016
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ASTOR: A Program Repair Library for Java. In Proceedings of the 25th International Symposium on Software Testing and Analysis . 441–444
Matias Martinez and Martin Monperrus. 2016 · 2016
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A Survey on Software Fault Localization
W. E. Wong, R. Gao, Y. Li, R. Abreu, and F. Wotawa. 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 (SPLASH Companion’17) . 55–56
Derrick Lin, James Koppel, Angela Chen, and Armando Solar-Lezama. 2017 · 2017
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Attention Is All You Need. In Advances in Neural Information Processing Systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Precise Condition Synthesis for Program Repair. In Proceedings of the 39th IEEE/ACM International Conference on Software Engineering . IEEE, 416–426
Yingfei Xiong, Jie Wang, Runfa Yan, Jiachen Zhang, Shi Han, Gang Huang, and Lu Zhang. 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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Ultra-Large Repair Search Space with Automatically Mined Templates: The Cardumen Mode of Astor. In Proceedings of the International Symposium on Search Based Software Engineering (SSBSE’18) . Springer, 65–86
Matias Martinez and Martin Monperrus. 2018 · 2018
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Automatic Software Repair: A Bibliography
Martin Monperrus. 2018 · 2018
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Identifying Patch Correctness in Test-Based Program Repair. In Proceedings of the 40th IEEE/ACM International Conference on Software Engineering . 789–799
Yingfei Xiong, Xinyuan Liu, Muhan Zeng, Lu Zhang, and Gang Huang. 2018 · 2018
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ARJA: Automated Repair of Java Programs Via Multi-objective Genetic Programming
Yuan Yuan and Wolfgang Banzhaf. 2018 · 2018
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BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT’19) . Association for Computational Linguistics, 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Automatic Software Repair: A Survey
Luca Gazzola, Daniela Micucci, and Leonardo Mariani. 2019 · 2019
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Re-Factoring Based Program Repair Applied to Programming Assignments. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE Computer Society, 388–398
Yang Hu, Umair Z Ahmed, Sergey Mechtaev, Ben Leong, and Abhik Roychoudhury. 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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A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries. In Proceedings of the 17th International Conference on Mining Software Repositories . 508–512
Jiahao Fan, Yi Li, Shaohua Wang, and Tien N Nguyen. 2020 · 2020
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics . 1536–1547
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020 · 2020
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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 (MSR’20) . 573–577
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 42nd ACM/IEEE International Conference on Software Engineering . 602–614
Yi Li, Shaohua Wang, and Tien N Nguyen. 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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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Noam Raffel, Colinand Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering . 981–992
Haoye Tian, Kui Liu, Abdoul Kader Kaboré, Anil Koyuncu, Li Li, Jacques Klein, and Tegawendé F Bissyandé. 2020 · 2020
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Unified Pre-Training for Program Understanding and Generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Association for Computational Linguistics, 2655–2668
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
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TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer. In International Conference on Machine Learning . PMLR, 780–791
Berkay Berabi, Jingxuan He, Veselin Raychev, and Martin Vechev. 2021 · 2021
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CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-source Software. In Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering . 30–39
Guru Bhandari, Amara Naseer, and Leon Moonen. 2021 · 2021
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Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021 · 2021
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DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons
Dawn Drain, Colin B Clement, Guillermo Serrato, and Neel Sundaresan. 2021a · 2021
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GraphCodeBERT: Pre-training Code Representations with Data Flow. In Proceedings of the 9th International Conference on Learning Representations . 1–18
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin B. Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. 2021 · 2021
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CURE: Code-Aware Neural Machine Translation for Automatic Program Repair. In Proceedings of the 43rd IEEE/ACM International Conference on Software Engineering . 1161–1173
Nan Jiang, Thibaud Lutellier, and Lin Tan. 2021 · 2021
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Interactive Patch Filtering as Debugging Aid. In 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 239–250
Jingjing Liang, Ruyi Ji, Jiajun Jiang, Shurui Zhou, Yiling Lou, Yingfei Xiong, and Gang Huang. 2021 · 2021
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CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin B. Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and Shujie Liu. 2021 · 2021
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Applying Codebert for Automated Program Repair of Java Simple Bugs. In Proceedings Companion of the 18th IEEE/ACM International Conference on Mining Software Repositories (MSR’21) . 505–509
Ehsan Mashhadi and Hadi Hemmati. 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 . 8696–8708
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 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
Qihao Zhu, Zeyu Sun, Yuan-an Xiao, Wenjie Zhang, Kang Yuan, Yingfei Xiong, and Lu Zhang. 2021 · 2021
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SynShine: Improved Fixing of Syntax Errors
Toufique Ahmed, Noah Rose Ledesma, and Premkumar Devanbu. 2022 · 2022
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GPT-Neox-20b: An Open-Source Autoregressive Language Model. In Proceedings of BigScience Episode# 5–Workshop on Challenges & Perspectives in Creating Large Language Models . 95–136
Sidney Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, et al · 2022
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TOGA: A Neural Method for Test Oracle Generation. In Proceedings of the 44th International Conference on Software Engineering . ACM, 2130–2141
Elizabeth Dinella, Gabriel Ryan, Todd Mytkowicz, and Shuvendu K Lahiri. 2022 · 2022
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VulRepair: A T5-Based Automated Software Vulnerability Repair. In the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . ACM, 935–947
Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Van Nguyen, and Phung Dinh. 2022 · 2022
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DeepDev-PERF: A Deep Learning-Based Approach for Improving Software Performance. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 948–958
Spandan Garg, Roshanak Zilouchian Moghaddam, Colin B Clement, Neel Sundaresan, and Chen Wu. 2022 · 2022
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UniXcoder: Unified Cross-Modal Pre-training for Code Representation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 7212–7225
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
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Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar. In Deep Learning for Code Workshop
Yaojie Hu, Xingjian Shi, Qiang Zhou, and Lee Pike. 2022 · 2022
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An Empirical Study of Deep Transfer Learning-Based Program Repair for Kotlin Projects. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1441–1452
Misoo Kim, Youngkyoung Kim, Hohyeon Jeong, Jinseok Heo, Sungoh Kim, Hyunhee Chung, and Eunseok Lee. 2022 · 2022
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Towards Javascript Program Repair with Generative Pre-trained Transformer. In 2022 IEEE/ACM International Workshop on Automated Program Repair . IEEE, 61–68
Márk Lajkó, Viktor Csuvik, and László Vidács. 2022 · 2022
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Solving Quantitative Reasoning Problems with Language Models. In Advances in Neural Information Processing Systems
Aitor Lewkowycz, Anders Johan Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Venkatesh Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, and Vedant Misra. 2022 · 2022
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The Living Review on Automated Program Repair
Martin Monperrus. 2022 · 2022
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Can OpenAI’s Codex Fix Bugs? An Evaluation on QuixBugs. In Proceedings of the Third International Workshop on Automated Program Repair . 69–75
Julian Aron Prenner, Hlib Babii, and Romain Robbes. 2022 · 2022
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Framing Program Repair as Code Completion. In Proceedings of the Third International Workshop on Automated Program Repair . IEEE, 38–45
Francisco Ribeiro, Rui Abreu, and João Saraiva. 2022 · 2022
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Compressing Pre-Trained Models of Code into 3 Mb. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering . 1–12
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Is This Change the Answer to That Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness. In 37th IEEE/ACM International Conference on Automated Software Engineering . IEEE, 1–13
Haoye Tian, Xunzhu Tang, Andrew Habib, Shangwen Wang, Kui Liu, Xin Xia, Jacques Klein, and TegawendÉ F BissyandÉ. 2022 · 2022
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Machine/Deep Learning for Software Engineering: A Systematic Literature Review
Simin Wang, Liguo Huang, Amiao Gao, Jidong Ge, Tengfei Zhang, Haitao Feng, Ishna Satyarth, Ming Li, He Zhang, and Vincent Ng. 2022 · 2022
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A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
Cody Watson, Nathan Cooper, David Nader Palacio, Kevin Moran, and Denys Poshyvanyk. 2022 · 2022
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Let’s Talk with Developers, Not about Developers: A Review of Automatic Program Repair Research
Emily Winter, Vesna Nowack, David Bowes, Steve Counsell, Tracy Hall, Sæmundur Haraldsson, and John Woodward. 2022 · 2022
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Less Training, More Repairing Please: Revisiting Automated Program Repair Via Zero-Shot Learning. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 959–971
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Pre-Trained Model-Based Automated Software Vulnerability Repair: How Far are We?
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Yanming Yang, Xin Xia, David Lo, and John Grundy. 2022 · 2022
Cited alongside, same era.
SelfAPR: Self-Supervised Program Repair with Test Execution Diagnostics. In 2022 37th IEEE/ACM International Conference on Automated Software Engineering . IEEE
He Ye, Matias Martinez, Xiapu Luo, Tao Zhang, and Martin Monperrus. 2022b · 2022
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CIRCLE: Continual Repair across Programming Languages. In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis . ACM, 678–690
Wei Yuan, Quanjun Zhang, Tieke He, Chunrong Fang, Nguyen Quoc Viet Hung, Xiaodong Hao, and Hongzhi Yin. 2022 · 2022
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Repairing Bugs in Python Assignments Using Large Language Models
Jialu Zhang, José Cambronero, Sumit Gulwani, Vu Le, Ruzica Piskac, Gustavo Soares, and Gust Verbruggen. 2022a · 2022
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Program Repair: Automated vs. Manual
Quanjun Zhang, Yuan Zhao, Weisong Sun, Chunrong Fang, Ziyuan Wang, and Lingming Zhang. 2022c · 2022
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A3test: Assertion-Augmented Automated Test Case Generation
Saranya Alagarsamy, Chakkrit Tantithamthavorn, and Aldeida Aleti. 2023 · 2023
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Can LLMs Patch Security Issues?
Kamel Alrashedy and Abdullah Aljasser. 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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Quanjun Zhang, Chunrong Fang, Bowen Yu, Weisong Sun, Tongke Zhang, and Zhenyu Chen. 2023d · 2023
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GAMMA: Revisiting Template-based Automated Program Repair via Mask Prediction. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering . IEEE, 535–547
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Evaluating Pre-Trained Language Models for Repairing Api Misuses
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Program Repair by Fuzzing over Patch and Input Space
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Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code
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Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
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The Right Prompts for the Job: Repair Code-Review Defects with Large Language Model
Zelin Zhao, Zhaogui Xu, Jialong Zhu, Peng Di, Yuan Yao, and Xiaoxing Ma. 2023a · 2023
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PatchZero: Zero-Shot Automatic Patch Correctness Assessment
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Tare: Type-Aware Neural Program Repair. In 2023 IEEE/ACM 45th International Conference on Software Engineering . IEEE, 1443–1455
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SWE-bench: Can Language Models Resolve Real-world Github Issues?. In The Twelfth International Conference on Learning Representations
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