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Large language models have shown good potential in supporting software development tasks.
Where is the bug and how is it fixed? an experiment with practitioners
Böhme Marcel, Soremekun Ezekiel O, Chattopadhyay Sudipta, Ugherughe Emamurho, and Zeller Andreas · 2017
Earlier work this paper cites.
ACM SIGSOFT empirical standards
Paul Ralph, Sebastian Baltes, Domenico Bianculli, Yvonne Dittrich, Michael Felderer, Robert Feldt, …, and Sira Vegas · 2020
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Evaluating large language models trained on code, 2021
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan …, and Wojciech Zaremba · 2021
Earlier work this paper cites.
Program synthesis with large language models, 2021
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton · 2021
Earlier work this paper cites.
Benchmarking as empirical standard in software engineering research
Hasselbring Wilhelm · 2021
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Multi-lingual evaluation of code generation models
Athiwaratkun Ben, Gouda Sanjay Krishna, Wang Zijian, Li Xiaopeng, Tian Yuchen, Tan Ming, Ahmad Wasi Uddin, Wang Shiqi, Sun Qing, Shang Mingyue, et al · 2022
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Large language models are few-shot testers: Exploring llm-based general bug reproduction
Kang Sungmin, Yoon Juyeon, and Yoo Shin · 2023
Earlier work this paper cites.
Large language models in fault localisation
Wu Yonghao, Li Zheng, Zhang Jie M, Mike Papadakis, Mark Harman, and Yong Liu · 2023
Earlier work this paper cites.
Automated program repair in the era of large pre-trained language models
Xia Chunqiu Steven, Wei Yuxiang, and Zhang Lingming · 2023
Earlier work this paper cites.
Wizardcoder: Empowering code large language models with evol-instruct, 2023
Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang · 2023
Earlier work this paper cites.
Wizardlm: Empowering large language models to follow complex instructions
Xu Can, Sun Qingfeng, Zheng Kai, Geng Xiubo, Zhao Pu, Feng Jiazhan, Tao Chongyang, and Jiang Daxin · 2023
Earlier work this paper cites.
Starcoder: may the source be with you!, 2023
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, and Harm de Vries · 2023
Cited alongside, same era.
Towards reasoning in large language models: A survey
Huang Jie and Chang Kevin Chen-Chuan · 2023
Cited alongside, same era.
Nlp evaluation in trouble: On the need to measure llm data contamination for each benchmark
Oscar Sainz, Jon Ander Campos, Iker García-Ferrero, Julen Etxaniz, Oier Lopez de Lacalle, and Eneko Agirre · 2023
Cited alongside, same era.
Rethinking benchmark and contamination for language models with rephrased samples
Shuo Yang, Wei-Lin Chiang, Lianmin Zheng, Joseph E Gonzalez, and Ion Stoica · 2023
Cited alongside, same era.
Explainable automated debugging via large language model-driven scientific debugging
Kang Sungmin, Chen Bei, Yoo Shin, and Lou Jian-Guang · 2023
Cited alongside, same era.
A unified debugging approach via llm-based multi-agent synergy
Cheryl Lee, Chunqiu Steven Xia, Jen-tse Huang, Zhouruixin Zhu, Lingming Zhang, and Michael R Lyu · 2024
Closest in time.
A comprehensive study of the capabilities of large language models for vulnerability detection
Steenhoek Benjamin, Rahman Md Mahbubur, Roy Monoshi Kumar, Alam Mirza Sanjida, Barr Earl T, and Le Wei · 2024
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How much are llms contaminated? a comprehensive survey and the llmsanitize library
Mathieu Ravaut, Bosheng Ding, Fangkai Jiao, Hailin Chen, Xingxuan Li, Ruochen Zhao, Chengwei Qin, Caiming Xiong, and Shafiq Joty · 2024
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Leak, cheat, repeat: Data contamination and evaluation malpractices in closed-source llms
Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, and Ondřej Dušek · 2024
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An analysis of the automatic bug fixing performance of chatgpt
Sobania Dominik, Briesch Martin, Hanna Carol, and Petke Justyna · 2023
Cited alongside, same era.
Beyond code generation: An observational study of chatgpt usage in software engineering practice
Khojah Ranim, Mohamad Mazen, Leitner Philipp, and Neto Francisco Gomes de Oliveira · 2024
Cited alongside, same era.
Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation
Liu Jiawei, Xia Chunqiu Steven, Wang Yuyao, and Zhang Lingming · 2024
Cited alongside, same era.
Code llama: Open foundation models for code, 2024
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, …, and Gabriel Synnaeve · 2024
Cited alongside, same era.
Deepseek-coder: When the large language model meets programming – the rise of code intelligence, 2024
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y.K. Li, Fuli Luo, Yingfei Xiong, and Wenfeng Liang · 2024
Cited alongside, same era.
Debugbench: Evaluating debugging capability of large language models, 2024
Runchu Tian, Yining Ye, Yujia Qin, Xin Cong, Yankai Lin, Yinxu Pan, Yesai Wu, Zhiyuan Liu, and Maosong Sun · 2024
Cited alongside, same era.
Phind, phind/phind-codellama-34b-v2 - hugging face
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Cited in the paper.
Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica · 2024
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Concerned with data contamination? assessing countermeasures in code language model
Jialun Cao, Wuqi Zhang, and Shing-Chi Cheung · 2024
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Prompting is all you need: Automated android bug replay with large language models
Feng, Sidong, Chen, and Chunyang · 2024
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Ldb: A large language model debugger via verifying runtime execution step-by-step
Lily Zhong, Zilong Wang, and Jingbo Shang · 2024
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Panda: Performance debugging for databases using llm agents
Singh Vikramank, Vaidya Kapil Eknath, Kumar Vinayshekhar Bannihatti, Khosla Sopan, Narayanaswamy Murali, Gangadharaiah Rashmi, and Kraska Tim · 2024
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Repairagent: An autonomous, llm-based agent for program repair
Bouzenia Islem, Devanbu Premkumar, and Pradel Michael · 2024
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