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Large Language Models (LLMs) have transformed software development by enabling code generation, automated debugging, and complex reasoning.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Unsupervised translation of programming languages
Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample · 2020
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Unified pre-training for program understanding and generation
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
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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
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CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi · 2021
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Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le · 2022
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Less training, more repairing please: revisiting automated program repair via zero-shot learning
Chunqiu Steven Xia and Lingming Zhang · 2022
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Learning to break the loop: Analyzing and mitigating repetitions for neural text generation
Jin Xu, Xiaojiang Liu, Jianhao Yan, Deng Cai, Huayang Li, and Jian Li · 2022
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Summarize and generate to back-translate: Unsupervised translation of programming languages
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2023
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AVATAR: A parallel corpus for Java-python program translation
Wasi Uddin Ahmad, Md Golam Rahman Tushar, Saikat Chakraborty, and Kai-Wei Chang · 2023
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Multi-lingual evaluation of code generation models
Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, Sujan Kumar Gonugondla, Hantian Ding, Varun Kumar, Nathan Fulton, Arash Farahani, Siddhartha Jain, Robert Giaquinto, Haifeng Qian, Murali Krishna Ramanathan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, and Bing Xiang · 2023
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Code alpaca: An instruction-following llama model for code generation
Sahil Chaudhary · 2023
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Large language models for compiler optimization
Chris Cummins, Volker Seeker, Dejan Grubisic, Mostafa Elhoushi, Youwei Liang, Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Kim Hazelwood, Gabriel Synnaeve, et al · 2023
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Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models
Yinlin Deng, Chunqiu Steven Xia, Haoran Peng, Chenyuan Yang, and Lingming Zhang · 2023
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Crosscodeeval: A diverse and multilingual benchmark for cross-file code completion
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
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Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, et al · 2023
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Fixeval: Execution-based evaluation of program fixes for programming problems
Md Mahim Anjum Haque, Wasi Uddin Ahmad, Ismini Lourentzou, and Chris Brown · 2023
Cited alongside, same era.
Impact of code language models on automated program repair
Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan · 2023
Cited alongside, same era.
Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models
Caroline Lemieux, Jeevana Priya Inala, Shuvendu K Lahiri, and Siddhartha Sen · 2023
Cited alongside, same era.
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 · 2023
Cited alongside, same era.
Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2023
Cited alongside, same era.
Qwen2. 5-coder technical report
Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, et al · 2024
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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 R Narasimhan · 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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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 · 2024
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Genetic instruct: Scaling up synthetic generation of coding instructions for large language models
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Understanding the effectiveness of large language models in code translation
Rangeet Pan, Ali Reza Ibrahimzada, Rahul Krishna, Divya Sankar, Lambert Pouguem Wassi, Michele Merler, Boris Sobolev, Raju Pavuluri, Saurabh Sinha, and Reyhaneh Jabbarvand · 2023
Cited alongside, same era.
Evol-teacher: Recreating wizardcoder
Nick Roshdieh · 2023
Cited alongside, same era.
Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, 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 · 2023
Cited alongside, same era.
Tigerbot kaggle leetcode solutions dataset (english) - 2k
TigerResearch · 2023
Cited alongside, same era.
Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi · 2023
Cited alongside, same era.
Copiloting the copilots: Fusing large language models with completion engines for automated program repair
Yuxiang Wei, Chunqiu Steven Xia, and Lingming Zhang · 2023
Cited alongside, same era.
Somshubra Majumdar, Vahid Noroozi, Sean Narenthiran, Aleksander Ficek, Jagadeesh Balam, and Boris Ginsburg · 2024
Later among the works it cites.
SWT-bench: Testing and validating real-world bug-fixes with code agents
Niels Mündler, Mark Niklas Mueller, Jingxuan He, and Martin Vechev · 2024
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Learning performance-improving code edits
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
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Automated program repair via conversation: Fixing 162 out of 337 bugs for 0.42 0.42 each using chatgpt
Chunqiu Steven Xia and Lingming Zhang · 2024
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WizardLM: Empowering large pre-trained language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Qingwei Lin, and Daxin Jiang · 2024
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Understanding in-context learning from repetitions
Jianhao Yan, Jin Xu, Chiyu Song, Chenming Wu, Yafu Li, and Yue Zhang · 2024
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WaveCoder: Widespread and versatile enhancement for code large language models by instruction tuning
Zhaojian Yu, Xin Zhang, Ning Shang, Yangyu Huang, Can Xu, Yishujie Zhao, Wenxiang Hu, and Qiufeng Yin · 2024
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Evaluating and improving chatgpt for unit test generation
Zhiqiang Yuan, Mingwei Liu, Shiji Ding, Kaixin Wang, Yixuan Chen, Xin Peng, and Yiling Lou · 2024
Later among the works it cites.
OpenCodeInterpreter: Integrating code generation with execution and refinement
Tianyu Zheng, Ge Zhang, Tianhao Shen, Xueling Liu, Bill Yuchen Lin, Jie Fu, Wenhu Chen, and Xiang Yue · 2024
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Scoring verifiers: Evaluating synthetic verification in code and reasoning, 2025
Aleksander Ficek, Somshubra Majumdar, Vahid Noroozi, and Boris Ginsburg · 2025
Closest in time.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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Livecodebench: Holistic and contamination free evaluation of large language models for code
Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica · 2025
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Swe-lancer: Can frontier llms earn $1 million from real-world freelance software engineering?
Samuel Miserendino, Michele Wang, Tejal Patwardhan, and Johannes Heidecke · 2025
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Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
Terry Yue Zhuo, Vu Minh Chien, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, Simon Brunner, Chen GONG, James Hoang, Armel Randy Zebaze, Xiaoheng Hong, Wen-Ding Li, Jean Kaddour, Ming Xu, Zhihan Zhang, Prateek Yadav, Naman Jain, Alex Gu, Zhoujun Cheng, Jiawei Liu, Qian Liu, Zijian Wang, David Lo, Binyuan Hui, Niklas Muennighoff, Daniel Fried, Xiaoning Du, Harm de Vries, and Leandro Von Werra · 2025
Closest in time.