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Code generation has largely improved development efficiency in the era of large language models (LLMs).
Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?
Madeline Endres, Sarah Fakhoury, Saikat Chakraborty, and Shuvendu K Lahiri. 2024 · 1912
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Accuracy of performance counter measurements. In 2009 IEEE International Symposium on Performance Analysis of Systems and Software . 23–32
Dmitrijs Zaparanuks, Milan Jovic, and Matthias Hauswirth. 2009 · 2009
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Computer Organization and Design - The Hardware / Software Interface (Revised 4th Edition)
David A. Patterson and John L. Hennessy. 2012 · 2012
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Dynamically reconfiguring software microbenchmarks: reducing execution time without sacrificing result quality. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Virtual Event, USA) (ESEC/FSE 2020) . Association for Computing Machinery, New York, NY, USA, 989–1001
Christoph Laaber, Stefan Würsten, Harald C. Gall, and Philipp Leitner. 2020 · 2020
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Program Synthesis with Large Language Models
Jacob Austin, Augustus Odena, Maxwell I. Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, and Charles Sutton. 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, et al · 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, et al · 2021
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Measuring Coding Challenge Competence With APPS. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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Retrieval-Augmented Generation for Code Summarization via Hybrid GNN. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 . OpenReview.net
Shangqing Liu, Yu Chen, Xiaofei Xie, Jing Kai Siow, and Yang Liu. 2021 · 2021
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Retrieval Augmented Code Generation and Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 16-20 November, 2021 . Association for Computational Linguistics, 2719–2734
Md. Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 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, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021 . Association for Computational Linguistics, 8696–8708
Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi. 2021 · 2021
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Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, et al · 2022
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GitHub Octoverse report on programming languages
Inc. GitHub. 2022 · 2022
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CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu-Hong Hoi. 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, 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
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Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, et al · 2022
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Emergent Abilities of Large Language Models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022 · 2022
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SantaCoder: don’t reach for the stars!
Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Muñoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, et al · 2023
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Shreya Bhatia, Tarushi Gandhi, Dhruv Kumar, and Pankaj Jalote. 2023 · 2023
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Teaching Large Language Models to Self-Debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2023 · 2023
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Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2023, Seattle, WA, USA, July 17-21, 2023 . ACM, 423–435
Yinlin Deng, Chunqiu Steven Xia, Haoran Peng, Chenyuan Yang, and Lingming Zhang. 2023 · 2023
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CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023
Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Hantian Ding, Ming Tan, Nihal Jain, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, and Bing Xiang. 2023 · 2023
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InCoder: A Generative Model for Code Infilling and Synthesis. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, and Mike Lewis. 2023 · 2023
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Sanitized version of MBPP benchmark released by Google
Google. 2023 · 2023
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L2MAC: Large Language Model Automatic Computer for Unbounded Code Generation
Samuel Holt, Max Ruiz Luyten, and Mihaela van der Schaar. 2023 · 2023
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AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation
Dong Huang, Qingwen Bu, Jie M. Zhang, Michael Luck, and Heming Cui. 2023 · 2023
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SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. 2023 · 2023
Earlier work this paper cites.
Mohammad Abdullah Matin Khan, M. Saiful Bari, Xuan Long Do, Weishi Wang, Md. Rizwan Parvez, and Shafiq R. Joty. 2023 · 2023
Cited alongside, same era.
StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al · 2023
Cited alongside, same era.
RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems
Tianyang Liu, Canwen Xu, and Julian J. McAuley. 2023c · 2023
Cited alongside, same era.
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2023 · 2023
Cited alongside, same era.
API reference provided by Google
Google. 2024 · 2024
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DeepSeek-Coder: When the Large Language Model Meets Programming - The Rise of Code Intelligence
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 · 2024
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REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark
Nam Le Hai, Dung Manh Nguyen, and Nghi D. Q. Bui. 2024 · 2024
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MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net
Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, et al · 2024
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LEVER: Learning to Verify Language-to-Code Generation with Execution. In International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA (Proceedings of Machine Learning Research, Vol. 202) . PMLR, 26106–26128
Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-Tau Yih, Sida I. Wang, and Xi Victoria Lin. 2023 · 2023
Cited alongside, same era.
CodeGen2: Lessons for Training LLMs on Programming and Natural Languages
Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, and Yingbo Zhou. 2023a · 2023
Cited alongside, same era.
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2023b · 2023
Cited alongside, same era.
OpenAI. 2023 · 2023
Cited alongside, same era.
Code Llama: Open Foundation Models for Code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
Cited alongside, same era.
An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation
Max Schäfer, Sarah Nadi, Aryaz Eghbali, and Frank Tip. 2024 · 2023
Cited alongside, same era.
Reflexion: language agents with verbal reinforcement learning. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023 · 2023
Cited alongside, same era.
Towards effective assessment of steady state performance in Java software: are we there yet?
Luca Traini, Vittorio Cortellessa, Daniele Di Pompeo, and Michele Tucci. 2023 · 2023
Cited alongside, same era.
Soneya Binta Hossain and Matthew B. Dwyer. 2024 · 2024
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EffiBench: Benchmarking the Efficiency of Automatically Generated Code
Dong Huang, Jie M. Zhang, Yuhao Qing, and Heming Cui. 2024 · 2024
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Deep Infra API
Deep Infra. 2024 · 2024
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Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
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Harnessing the Power of LLMs: Automating Unit Test Generation for High-Performance Computing
Rabimba Karanjai, Aftab Hussain, Md Rafiqul Islam Rabin, Lei Xu, Weidong Shi, and Mohammad Amin Alipour. 2024 · 2024
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Large Language Models as Test Case Generators: Performance Evaluation and Enhancement
Kefan Li and Yuan Yuan. 2024 · 2024
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LLM-Powered Test Case Generation for Detecting Tricky Bugs
Kaibo Liu, Yiyang Liu, Zhenpeng Chen, Jie M. Zhang, Yudong Han, Yun Ma, Ge Li, and Gang Huang. 2024a · 2024
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StarCoder 2 and The Stack v2: The Next Generation
Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, et al · 2024
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OctoPack: Instruction Tuning Code Large Language Models. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net
Niklas Muennighoff, Qian Liu, Armel Randy Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, and Shayne Longpre. 2024 · 2024
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Code Agents are State of the Art Software Testers
Niels Mündler, Mark Niklas Müller, Jingxuan He, and Martin T. Vechev. 2024 · 2024
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Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation
Wendkûuni C. Ouédraogo, Kader Kaboré, Haoye Tian, Yewei Song, Anil Koyuncu, Jacques Klein, David Lo, and Tegawendé F. Bissyandé. 2024 · 2024
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PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback
Yun Peng, Akhilesh Deepak Gotmare, Michael Lyu, Caiming Xiong, Silvio Savarese, and Doyen Sahoo. 2024 · 2024
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Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
Tal Ridnik, Dedy Kredo, and Itamar Friedman. 2024 · 2024
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A Tool for Test Case Scenarios Generation Using Large Language Models
Malik Abdul Sami, Zeeshan Rasheed, Muhammad Waseem, Zheying Zhang, Tomas Herda, and Pekka Abrahamsson. 2024 · 2024
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Learning Performance-Improving Code Edits. In The Twelfth International Conference on Learning Representations
Alexander G Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, and Amir Yazdanbakhsh. 2024 · 2024
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The Cirron library
Matt Stuchlik, Bruno P. Kinoshita, and Donald Lee. 2024 · 2024
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ARKS: Active Retrieval in Knowledge Soup for Code Generation
Hongjin Su, Shuyang Jiang, Yuhang Lai, Haoyuan Wu, Boao Shi, Che Liu, Qian Liu, and Tao Yu. 2024 · 2024
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TESTEVAL: Benchmarking Large Language Models for Test Case Generation
Wenhan Wang, Chenyuan Yang, Zhijie Wang, Yuheng Huang, Zhaoyang Chu, Da Song, Lingming Zhang, An Ran Chen, and Lei Ma. 2024b · 2024
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Executable Code Actions Elicit Better LLM Agents
Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji. 2024a · 2024
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SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. 2024 · 2024
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Where Are Large Language Models for Code Generation on GitHub?
Xiao Yu, Lei Liu, Xing Hu, Jacky Wai Keung, Jin Liu, and Xin Xia. 2024 · 2024
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AutoCodeRover: Autonomous Program Improvement
Yuntong Zhang, Haifeng Ruan, Zhiyu Fan, and Abhik Roychoudhury. 2024 · 2024
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DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
Qihao Zhu, Daya Guo, Zhihong Shao, Dejian Yang, Peiyi Wang, Runxin Xu, Y Wu, Yukun Li, Huazuo Gao, Shirong Ma, et al · 2024
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