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A Survey on Large Language Models for Code Generation

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2406.00515

Summary

This survey covers code generation with large language models: turning a natural-language request into working source code (NL2Code). Tools such as GitHub Copilot, CodeGeeX and Amazon CodeWhisperer depend on this task. Earlier reviews either spread across many code tasks or only covered models released between 2020 and 2022, so newer techniques had no dedicated overview. The authors fill that gap with a systematic literature review built around a taxonomy that follows the life of a code LLM. It covers how training data is collected and synthesized, then pre-training, instruction tuning, alignment with feedback, prompting, retrieval augmentation, autonomous coding agents and LLM-as-a-Judge. It also covers evaluation, ethics, environmental cost and real-world deployment. They trace how the field developed and compare models on HumanEval, MBPP and BigCodeBench, showing steady gains in capability. Finally, they list the open problems that keep research results from carrying over into everyday software engineering. NLP and SE researchers get one up-to-date entry point, which a companion GitHub repository keeps current.

Key points

  • On the HumanEval leaderboard, pass@1 rose from 3.6% (PaLM 8B) to 95.1% (LDB). HumanEval serves as the de facto standard benchmark for coding ability.
  • The models reviewed include general-purpose LLMs (ChatGPT, GPT-4, LLaMA, Claude 3) and code-specific ones (StarCoder, Code LLaMA, DeepSeek-Coder, CodeGemma, CodeQwen, WizardCoder). Qwen2.5-Coder is noted for training on synthetic data.
  • Code LLMs come in encoder-only, decoder-only and encoder-decoder forms. They are pre-trained mostly on code, with smaller amounts of text and math data.
  • Open challenges include:
    • benchmarks that measure more than functional correctness (efficiency, style, readability, maintainability);
    • support for low-resource, low-level and domain-specific programming languages;
    • continual learning, since retrieval only partly fixes outdated knowledge;
    • code safety and alignment with human preferences.
  • The companion resource is github.com/juyongjiang/CodeLLMSurvey.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

145 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 144 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1145/3747588 · arXiv 2406.00515

@misc{jiang2024survey,
  title = {{A Survey on Large Language Models for Code Generation}},
  author = {Jiang, Juyong and Wang, Fan and Shen, Jiasi and Kim, Sungju and Kim, Sunghun},
  year = {2024},
  doi = {10.1145/3747588},
  eprint = {2406.00515},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/3747588}
}

References

334 references, 74 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. 2023. AgentGPT: Assemble, configure, and deploy autonomous AI Agents in your browser. https://github.com/reworkd/AgentGPT. ↗
  2. 2023. AutoGPT is the vision of accessible AI for everyone, to use and to build on. https://github.com/Significant-Gravitas/AutoGPT. ↗
  3. 2023. BabyAGI. https://github.com/yoheinakajima/babyagi. ↗
  4. Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al. 2024. Phi-3 technical report: A highly capable language model locally on your phone. arXiv preprint arXiv:2404.14219 (2024). find our card
  5. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). find our card
  6. Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2020. A Transformer-based Approach for Source Code Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 4998–5007. find
  7. Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021. Unified pre-training for program understanding and generation. arXiv preprint arXiv:2103.06333 (2021). find our card
  8. Ali Al-Kaswan, Maliheh Izadi, and Arie Van Deursen. 2024. Traces of memorisation in large language models for code. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering. 1–12. find
  9. Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, et al. 2023. SantaCoder: don’t reach for the stars! arXiv preprint arXiv:2301.03988 (2023). find our card
  10. Miltiadis Allamanis and Charles Sutton. 2014. Mining idioms from source code. In Proceedings of the 22nd acm sigsoft international symposium on foundations of software engineering. 472–483. find
  11. Google DeepMind AlphaCode Team. 2023. AlphaCode 2 Technical Report. https://storage.googleapis.com/deepmind-media/AlphaCode2/AlphaCode2\_Tech\_Report.pdf. ↗
  12. Amazon. 2022. What is CodeWhisperer? https://docs.aws.amazon.com/codewhisperer/latest/userguide/what-is-cwspr.html. ↗
  13. Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019. MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computation… find our card
  14. Anthropic. 2024. The Claude 3 Model Family: Opus, Sonnet, Haiku. https://www-cdn.anthropic.com/de8ba9b01c9ab7cbabf5c33b80b7bbc618857627/Model\_Card\_Claude\_3.pdf. ↗
  15. Luca Ardito, Riccardo Coppola, Luca Barbato, and Diego Verga. 2020. A tool-based perspective on software code maintainability metrics: a systematic literature review. Scientific Programming 2020 (2020), 1–26. find
  16. Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, et al. 2022. Multi-lingual evaluation of code generation models. arXiv preprint arXiv:2210.14868 (2022). find
  17. Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. 2021. Program synthesis with large language models. arXiv preprint arXiv:2108.07732 (2021). find our card
  18. Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016). find our card
  19. Hannah McLean Babe, Sydney Nguyen, Yangtian Zi, Arjun Guha, Molly Q Feldman, and Carolyn Jane Anderson. 2023. StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code. arXiv:2306.04556 [cs.LG] find
  20. Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023. Qwen technical report. arXiv preprint arXiv:2309.16609 (2023). find our card
  21. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. 2022. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073 (2022). find our card
  22. Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade, Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, B Ashok, Shashank Shet, et al. 2023. Codeplan: Repository-level coding using llms and planning. arXiv preprint arXiv:2309.12499 (2023). — [arXiv:2309.12499](https://arxiv.org/abs/2309.12499) find
  23. Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization. 65–72. find our card
  24. Enrico Barbierato, Marco L Della Vedova, Daniele Tessera, Daniele Toti, and Nicola Vanoli. 2022. A methodology for controlling bias and fairness in synthetic data generation. Applied Sciences 12, 9 (2022), 4619. find
  25. Shraddha Barke, Michael B James, and Nadia Polikarpova. 2023. Grounded copilot: How programmers interact with code-generating models. Proceedings of the ACM on Programming Languages 7, OOPSLA1 (2023), 85–111. find
  26. Xiao Bi, Deli Chen, Guanting Chen, Shanhuang Chen, Damai Dai, Chengqi Deng, Honghui Ding, Kai Dong, Qiushi Du, Zhe Fu, et al. 2024. Deepseek llm: Scaling open-source language models with longtermism. arXiv preprint arXiv:2401.02954 (2024). find
  27. Zhangqian Bi, Yao Wan, Zheng Wang, Hongyu Zhang, Batu Guan, Fangxin Lu, Zili Zhang, Yulei Sui, Xuanhua Shi, and Hai Jin. 2024. Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback. arXiv preprint arXiv:2403.16792 (2024). find
  28. Christian Bird, Denae Ford, Thomas Zimmermann, Nicole Forsgren, Eirini Kalliamvakou, Travis Lowdermilk, and Idan Gazit. 2022. Taking Flight with Copilot: Early insights and opportunities of AI-powered pair-programming tools. Queue 20, 6 (2022), 35–57. find
  29. Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, et al. 2022. Gpt-neox-20b: An open-source autoregressive language model. arXiv preprint arXiv:2204.06745 (2022). find our card
  30. Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021. GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow. https://doi.org/10.5281/zenodo.5297715 If you use this software, please cite it using these metadata.. — [doi:10.5281/zenodo.5297715](https://doi.org/10.5281/zenodo.5297715) ↗
  31. Veronika Bogina, Alan Hartman, Tsvi Kuflik, and Avital Shulner-Tal. 2022. Educating software and AI stakeholders about algorithmic fairness, accountability, transparency and ethics. International Journal of Artificial Intelligence in Education (2022), 1–26. find
  32. Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021). find our card
  33. Tom B Brown. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020). find
  34. Raymond PL Buse and Westley R Weimer. 2009. Learning a metric for code readability. IEEE Transactions on software engineering 36, 4 (2009), 546–558. find
  35. Weilin Cai, Juyong Jiang, Le Qin, Junwei Cui, Sunghun Kim, and Jiayi Huang. 2024. Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts. arXiv preprint arXiv:2404.05019 (2024). — [arXiv:2404.05019](https://arxiv.org/abs/2404.05019) find
  36. Weilin Cai, Juyong Jiang, Fan Wang, Jing Tang, Sunghun Kim, and Jiayi Huang. 2024. A survey on mixture of experts. arXiv preprint arXiv:2407.06204 (2024). — [arXiv:2407.06204](https://arxiv.org/abs/2407.06204) find
  37. Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. 2021. Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21). 2633–2650. find our card
  38. Federico Cassano, John Gouwar, Francesca Lucchetti, Claire Schlesinger, Carolyn Jane Anderson, Michael Greenberg, Abhinav Jangda, and Arjun Guha. 2023. Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs. arXiv preprint arXiv:2308.09895 (2023). — [arXiv:2308.09895](https://arxiv.org/abs/2308.09895) find
  39. Federico Cassano, John Gouwar, Daniel Nguyen, Sydney Nguyen, Luna Phipps-Costin, Donald Pinckney, Ming-Ho Yee, Yangtian Zi, Carolyn Jane Anderson, Molly Q Feldman, et al. 2022. A scalable and extensible approach to benchmarking nl2code for 18 programming languages. arXiv preprint arXiv:2208.08227 (2… find
  40. Yekun Chai, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, and Hua Wu. 2022. ERNIE-Code: Beyond english-centric cross-lingual pretraining for programming languages. arXiv preprint arXiv:2212.06742 (2022). find
  41. Shubham Chandel, Colin B Clement, Guillermo Serrato, and Neel Sundaresan. 2022. Training and evaluating a jupyter notebook data science assistant. arXiv preprint arXiv:2201.12901 (2022). — [arXiv:2201.12901](https://arxiv.org/abs/2201.12901) find
  42. Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al. 2024. A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology 15, 3 (2024), 1–45. find our card
  43. Sahil Chaudhary. 2023. Code Alpaca: An Instruction-following LLaMA model for code generation. https://github. com/sahil280114/codealpaca. find
  44. Binger Chen and Ziawasch Abedjan. 2023. DUETCS: Code Style Transfer through Generation and Retrieval. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). IEEE, 2362–2373. find
  45. Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2022. Codet: Code generation with generated tests. arXiv preprint arXiv:2207.10397 (2022). find
  46. Fuxiang Chen, Fatemeh H Fard, David Lo, and Timofey Bryksin. 2022. On the transferability of pre-trained language models for low-resource programming languages. In Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension. 401–412. find
  47. Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2024. Benchmarking large language models in retrieval-augmented generation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 17754–17762. find our card
  48. 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. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021). find our card
  49. Stanley F Chen, Douglas Beeferman, and Roni Rosenfeld. 1998. Evaluation metrics for language models. (1998). find
  50. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. 2022. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. arXiv preprint arXiv:2211.12588 (2022). find our card
  51. Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2023. Teaching large language models to self-debug. arXiv preprint arXiv:2304.05128 (2023). find
  52. Xinyun Chen, Chang Liu, and Dawn Song. 2018. Tree-to-tree neural networks for program translation. Advances in neural information processing systems 31 (2018). find
  53. Andrew A Chien, Liuzixuan Lin, Hai Nguyen, Varsha Rao, Tristan Sharma, and Rajini Wijayawardana. 2023. Reducing the Carbon Impact of Generative AI Inference (today and in 2035). In Proceedings of the 2nd workshop on sustainable computer systems. 1–7. find
  54. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2023. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24, 240 (2023), 1–113. find
  55. Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta, Guchun Zhang, Yinpeng Guo, Zhongqi Li, Qi Zhang, Meng Xiao, Bo Shen, Lin Li, et al. 2022. Pangu-coder: Program synthesis with function-level language modeling. arXiv preprint arXiv:2207.11280 (2022). find
  56. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2024. Scaling instruction-finetuned language models. Journal of Machine Learning Research 25, 70 (2024), 1–53. find
  57. Colin B Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, and Neel Sundaresan. 2020. PyMT5: multi-mode translation of natural language and Python code with transformers. arXiv preprint arXiv:2010.03150 (2020). find
  58. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168 (2021). find our card
  59. CodeGemma Team, Ale Jakse Hartman, Andrea Hu, Christopher A. Choquette-Choo, Heri Zhao, Jane Fine, Jeffrey Hui, Jingyue Shen, Joe Kelley, Joshua Howland, Kshitij Bansal, Luke Vilnis, Mateo Wirth, Nam Nguyen, Paul Michel, Peter Choy, Pratik Joshi, Ravin Kumar, Sarmad Hashmi, Shubham Agrawal, Siqi Zuo… find
  60. Codeium. 2023. Free, ultrafast Copilot alternative for Vim and Neovim. https://github.com/Exafunction/codeium.vim. ↗
  61. Cognition. 2024. Introducing Devin, the first AI software engineer. https://www.cognition.ai/introducing-devin. ↗
  62. Trevor Cohn, Phil Blunsom, and Sharon Goldwater. 2010. Inducing tree-substitution grammars. The Journal of Machine Learning Research 11 (2010), 3053–3096. find
  63. Cognitive Computations. 2023. oa\_leet10k. https://huggingface.co/datasets/cognitivecomputations/oa\_leet10k. ↗
  64. Leonardo De Moura and Nikolaj Bjørner. 2008. Z3: An efficient SMT solver. In International conference on Tools and Algorithms for the Construction and Analysis of Systems. Springer, 337–340. find
  65. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2024. Qlora: Efficient finetuning of quantized llms. Advances in Neural Information Processing Systems 36 (2024). find
  66. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). find
  67. Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al. 2022. Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models. arXiv preprint arXiv:2203.06904 (2022). — [arXiv:2203.06904](https://arxiv.org/abs/2203.06904) find
  68. Yangruibo Ding, Zijian Wang, Wasi Ahmad, Hantian Ding, Ming Tan, Nihal Jain, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, et al. 2024. Crosscodeeval: A diverse and multilingual benchmark for cross-file code completion. Advances in Neural Information Processing Systems 36… find
  69. Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, and Bing Xiang. 2022. Cocomic: Code completion by jointly modeling in-file and cross-file context. arXiv preprint arXiv:2212.10007 (2022). find
  70. Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022. A survey on in-context learning. arXiv preprint arXiv:2301.00234 (2022). find our card
  71. Shihan Dou, Yan Liu, Haoxiang Jia, Limao Xiong, Enyu Zhou, Junjie Shan, Caishuang Huang, Wei Shen, Xiaoran Fan, Zhiheng Xi, et al. 2024. StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback. arXiv preprint arXiv:2402.01391 (2024). — [arXiv:2402.01391](https://arxiv.org/abs/2402.01391) find
  72. Xueying Du, Mingwei Liu, Kaixin Wang, Hanlin Wang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, and Yiling Lou. 2024. Evaluating large language models in class-level code generation. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering. 1–13. find
  73. Hugging Face. 2023. Training CodeParrot from Scratch. https://github.com/huggingface/blog/blob/main/codeparrot. md. ↗
  74. Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy, Shubho Sengupta, Shin Yoo, and Jie M Zhang. 2023. Large language models for software engineering: Survey and open problems. In 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE). IEEE, 3… find
  75. Zhiyu Fan, Xiang Gao, Martin Mirchev, Abhik Roychoudhury, and Shin Hwei Tan. 2023. Automated repair of programs from large language models. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). IEEE, 1469–1481. find
  76. Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al. 2020. Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155 (2020). find our card
  77. Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis. 2022. Incoder: A generative model for code infilling and synthesis. arXiv preprint arXiv:2204.05999 (2022). find
  78. Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. 2020. The pile: An 800gb dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027 (2020). find
  79. Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023. Pal: Program-aided language models. In International Conference on Machine Learning. PMLR, 10764–10799. find
  80. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997 (2023). find our card
  81. Linyuan Gong, Mostafa Elhoushi, and Alvin Cheung. 2024. AST-T5: Structure-Aware Pretraining for Code Generation and Understanding. arXiv preprint arXiv:2401.03003 (2024). find
  82. Alex Gu, Baptiste Rozière, Hugh Leather, Armando Solar-Lezama, Gabriel Synnaeve, and Sida I Wang. 2024. Cruxeval: A benchmark for code reasoning, understanding and execution. arXiv preprint arXiv:2401.03065 (2024). find our card
  83. Sumit Gulwani. 2010. Dimensions in program synthesis. In Proceedings of the 12th international ACM SIGPLAN symposium on Principles and practice of declarative programming. 13–24. find
  84. 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. Textbooks are all you need. arXiv preprint arXiv:2306.11644 (2023). find
  85. Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022. 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. find our card
  86. Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al. 2020. Graphcodebert: Pre-training code representations with data flow. arXiv preprint arXiv:2009.08366 (2020). find our card
  87. Daya Guo, Canwen Xu, Nan Duan, Jian Yin, and Julian McAuley. 2023. Longcoder: A long-range pre-trained language model for code completion. In International Conference on Machine Learning. PMLR, 12098–12107. find
  88. Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y Wu, YK Li, et al. 2024. DeepSeek-Coder: When the Large Language Model Meets Programming–The Rise of Code Intelligence. arXiv preprint arXiv:2401.14196 (2024). find
  89. Aman Gupta, Deepak Bhatt, and Anubha Pandey. 2021. Transitioning from Real to Synthetic data: Quantifying the bias in model. arXiv preprint arXiv:2105.04144 (2021). — [arXiv:2105.04144](https://arxiv.org/abs/2105.04144) find
  90. Aman Gupta, Anup Shirgaonkar, Angels de Luis Balaguer, Bruno Silva, Daniel Holstein, Dawei Li, Jennifer Marsman, Leonardo O Nunes, Mahsa Rouzbahman, Morris Sharp, et al. 2024. RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture. arXiv preprint arXiv:2401.08406 (2024). — [arXiv:2401.08406](https://arxiv.org/abs/2401.08406) find
  91. Hossein Hajipour, Keno Hassler, Thorsten Holz, Lea Schönherr, and Mario Fritz. 2024. CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models. In 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). IEEE, 684–709. find
  92. Perttu Hämäläinen, Mikke Tavast, and Anton Kunnari. 2023. Evaluating large language models in generating synthetic hci research data: a case study. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–19. find
  93. Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu. 2023. Reasoning with language model is planning with world model. arXiv preprint arXiv:2305.14992 (2023). find
  94. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778. find
  95. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. Measuring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103.03874 (2021). find our card
  96. Felipe Hoffa. 2016. GitHub on BigQuery: Analyze all the open source code. URL: https:// cloud.google.com/ blog/ topics/ public-datasets/ github-on-bigquery-analyze-all-the-open-source-code (2016). find
  97. Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. 2022. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556 (2022). find
  98. Samuel Holt, Max Ruiz Luyten, and Mihaela van der Schaar. 2023. L2MAC: Large Language Model Automatic Computer for Unbounded Code Generation. In The Twelfth International Conference on Learning Representations. find
  99. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751 (2019). find
  100. Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, et al. 2023. Metagpt: Meta programming for multi-agent collaborative framework. arXiv preprint arXiv:2308.00352 (2023). find our card
  101. Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang. 2024. Large Language Models for Software Engineering: A Systematic Literature Review. arXiv:2308.10620 [cs.SE] — [arXiv:2308.10620](https://arxiv.org/abs/2308.10620) find
  102. Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for NLP. In International conference on machine learning. PMLR, 2790–2799. find
  103. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021). find
  104. Dong Huang, Qingwen Bu, Jie M Zhang, Michael Luck, and Heming Cui. 2023. AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation. arXiv preprint arXiv:2312.13010 (2023). find
  105. Jie Huang and Kevin Chen-Chuan Chang. 2022. Towards reasoning in large language models: A survey. arXiv preprint arXiv:2212.10403 (2022). find our card
  106. Jie Huang and Kevin Chen-Chuan Chang. 2023. Towards Reasoning in Large Language Models: A Survey. In 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023. Association for Computational Linguistics (ACL), 1049–1065. find our card
  107. Junjie Huang, Chenglong Wang, Jipeng Zhang, Cong Yan, Haotian Cui, Jeevana Priya Inala, Colin Clement, Nan Duan, and Jianfeng Gao. 2022. Execution-based evaluation for data science code generation models. arXiv preprint arXiv:2211.09374 (2022). find
  108. Qiuyuan Huang, Naoki Wake, Bidipta Sarkar, Zane Durante, Ran Gong, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Noboru Kuno, Ade Famoti, et al. 2024. Position Paper: Agent AI Towards a Holistic Intelligence. arXiv preprint arXiv:2403.00833 (2024). — [arXiv:2403.00833](https://arxiv.org/abs/2403.00833) find
  109. Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Kai Dang, et al. 2024. Qwen2. 5-coder technical report. arXiv preprint arXiv:2409.12186 (2024). find our card
  110. Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019. Codesearchnet challenge: Evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436 (2019). find our card
  111. Yoichi Ishibashi and Yoshimasa Nishimura. 2024. Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization. arXiv preprint arXiv:2404.02183 (2024). — [arXiv:2404.02183](https://arxiv.org/abs/2404.02183) find
  112. Shu Ishida, Gianluca Corrado, George Fedoseev, Hudson Yeo, Lloyd Russell, Jamie Shotton, João F Henriques, and Anthony Hu. 2024. LangProp: A code optimization framework using Language Models applied to driving. arXiv preprint arXiv:2401.10314 (2024). — [arXiv:2401.10314](https://arxiv.org/abs/2401.10314) find
  113. Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2018. Mapping Language to Code in Programmatic Context. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 1643–1652. find
  114. Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, et al. 2022. Opt-iml: Scaling language model instruction meta learning through the lens of generalization. arXiv preprint arXiv:2212.12017 (2022). find our card
  115. Joel Jang, Seonghyeon Ye, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, Jungkyu Choi, and Minjoon Seo. 2022. Towards Continual Knowledge Learning of Language Models. In 10th International Conference on Learning Representations, ICLR 2022. International Conference on Learning Representations… find
  116. Fred Jelinek, Robert L Mercer, Lalit R Bahl, and James K Baker. 1977. Perplexity—a measure of the difficulty of speech recognition tasks. The Journal of the Acoustical Society of America 62, S1 (1977), S63–S63. find
  117. Susmit Jha, Sumit Gulwani, Sanjit A Seshia, and Ashish Tiwari. 2010. Oracle-guided component-based program synthesis. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering-Volume 1. 215–224. find
  118. Jiaming Ji, Tianyi Qiu, Boyuan Chen, Borong Zhang, Hantao Lou, Kaile Wang, Yawen Duan, Zhonghao He, Jiayi Zhou, Zhaowei Zhang, et al. 2023. Ai alignment: A comprehensive survey. arXiv preprint arXiv:2310.19852 (2023). find
  119. Ruyi Ji, Jingjing Liang, Yingfei Xiong, Lu Zhang, and Zhenjiang Hu. 2020. Question selection for interactive program synthesis. In Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation. 1143–1158. find
  120. Zhenlan Ji, Pingchuan Ma, Zongjie Li, and Shuai Wang. 2023. Benchmarking and explaining large language model-based code generation: A causality-centric approach. arXiv preprint arXiv:2310.06680 (2023). — [arXiv:2310.06680](https://arxiv.org/abs/2310.06680) find
  121. Juyong Jiang and Sunghun Kim. 2023. CodeUp: A Multilingual Code Generation Llama2 Model with Parameter-Efficient Instruction-Tuning. https://github.com/juyongjiang/CodeUp. ↗
  122. Shuyang Jiang, Yuhao Wang, and Yu Wang. 2023. Selfevolve: A code evolution framework via large language models. arXiv preprint arXiv:2306.02907 (2023). — [arXiv:2306.02907](https://arxiv.org/abs/2306.02907) find
  123. Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R Narasimhan. 2023. SWE-bench: Can Language Models Resolve Real-world Github Issues?. In The Twelfth International Conference on Learning Representations. find our card
  124. Alexander Wettig Kilian Lieret Shunyu Yao Karthik Narasimhan Ofir Press John Yang, Carlos E. Jimenez. 2024. SWE-AGENT: AGENT-COMPUTER INTERFACES ENABLE AUTOMATED SOFTWARE ENGINEERING. (2024). https://swe-agent.com/ ↗ our card
  125. Aravind Joshi and Owen Rambow. 2003. A formalism for dependency grammar based on tree adjoining grammar. In Proceedings of the Conference on Meaning-text Theory. MTT Paris, France, 207–216. find
  126. Harshit Joshi, José Cambronero Sanchez, Sumit Gulwani, Vu Le, Gust Verbruggen, and Ivan Radiček. 2023. Repair is nearly generation: Multilingual program repair with llms. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 5131–5140. find
  127. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361 (2020). find
  128. Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, Weishi Wang, Md Rizwan Parvez, and Shafiq Joty. 2023. xcodeeval: A large scale multilingual multitask benchmark for code understanding, generation, translation and retrieval. arXiv preprint arXiv:2303.03004 (2023). find
  129. Dahyun Kim, Yungi Kim, Wonho Song, Hyeonwoo Kim, Yunsu Kim, Sanghoon Kim, and Chanjun Park. 2024. sDPO: Don’t Use Your Data All at Once. arXiv preprint arXiv:2403.19270 (2024). find
  130. Dahyun Kim, Chanjun Park, Sanghoon Kim, Wonsung Lee, Wonho Song, Yunsu Kim, Hyeonwoo Kim, Yungi Kim, Hyeonju Lee, Jihoo Kim, et al. 2023. Solar 10.7 b: Scaling large language models with simple yet effective depth up-scaling. arXiv preprint arXiv:2312.15166 (2023). find
  131. Barbara Kitchenham, O Pearl Brereton, David Budgen, Mark Turner, John Bailey, and Stephen Linkman. 2009. Systematic literature reviews in software engineering–a systematic literature review. Information and software technology 51, 1 (2009), 7–15. find
  132. Denis Kocetkov, Raymond Li, LI Jia, Chenghao Mou, Yacine Jernite, Margaret Mitchell, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, et al. 2022. The Stack: 3 TB of permissively licensed source code. Transactions on Machine Learning Research (2022). find
  133. Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi Rui Tam, Keith Stevens, Abdullah Barhoum, Duc Nguyen, Oliver Stanley, Richárd Nagyfi, et al. 2024. Openassistant conversations-democratizing large language model alignment. Advances in Neural Information Processing Systems 36… find our card
  134. Bonan Kou, Shengmai Chen, Zhijie Wang, Lei Ma, and Tianyi Zhang. 2023. Is model attention aligned with human attention? an empirical study on large language models for code generation. arXiv preprint arXiv:2306.01220 (2023). — [arXiv:2306.01220](https://arxiv.org/abs/2306.01220) find
  135. Marie-Anne Lachaux, Baptiste Roziere, Lowik Chanussot, and Guillaume Lample. 2020. Unsupervised translation of programming languages. arXiv preprint arXiv:2006.03511 (2020). find our card
  136. Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Wen-tau Yih, Daniel Fried, Sida Wang, and Tao Yu. 2023. DS-1000: A natural and reliable benchmark for data science code generation. In International Conference on Machine Learning. PMLR, 18319–18345. find
  137. Hugo Laurençon, Lucile Saulnier, Thomas Wang, Christopher Akiki, Albert Villanova del Moral, Teven Le Scao, Leandro Von Werra, Chenghao Mou, Eduardo González Ponferrada, Huu Nguyen, et al. 2022. The bigscience roots corpus: A 1.6 tb composite multilingual dataset. Advances in Neural Information Proc… find
  138. Moritz Laurer. 2024. Synthetic data: save money, time and carbon with open source. https://huggingface.co/blog/synthetic-data-save-costs. ↗
  139. Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu Hong Hoi. 2022. Coderl: Mastering code generation through pretrained models and deep reinforcement learning. Advances in Neural Information Processing Systems 35 (2022), 21314–21328. find our card
  140. Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2023. Bloom: A 176b-parameter open-access multilingual language model. (2023). find
  141. Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Lu, Thomas Mesnard, Colton Bishop, Victor Carbune, and Abhinav Rastogi. 2023. Rlaif: Scaling reinforcement learning from human feedback with ai feedback. arXiv preprint arXiv:2309.00267 (2023). find
  142. Brian Lester, Rami Al-Rfou, and Noah Constant. 2021. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691 (2021). find
  143. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33 (2020), 9459–… find
  144. Jia Li, Ge Li, Xuanming Zhang, Yihong Dong, and Zhi Jin. 2024. EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories. arXiv preprint arXiv:2404.00599 (2024). — [arXiv:2404.00599](https://arxiv.org/abs/2404.00599) find
  145. Jia Li, Yunfei Zhao, Yongmin Li, Ge Li, and Zhi Jin. 2023. Towards enhancing in-context learning for code generation. arXiv preprint arXiv:2303.17780 (2023). — [arXiv:2303.17780](https://arxiv.org/abs/2303.17780) find
  146. Li Li, Tegawendé F Bissyandé, Mike Papadakis, Siegfried Rasthofer, Alexandre Bartel, Damien Octeau, Jacques Klein, and Le Traon. 2017. Static analysis of android apps: A systematic literature review. Information and Software Technology 88 (2017), 67–95. find
  147. Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al. 2023. Starcoder: may the source be with you! arXiv preprint arXiv:2305.06161 (2023). find
  148. Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. AlpacaEval: An Automatic Evaluator of Instruction-following Models. https://github.com/tatsu-lab/alpaca\_eval. ↗
  149. Xiang Lisa Li and Percy Liang. 2021. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190 (2021). find
  150. Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, and Yin Tat Lee. 2023. Textbooks are all you need ii: phi-1.5 technical report. arXiv preprint arXiv:2309.05463 (2023). find our card
  151. Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al. 2022. Competition-level code generation with alphacode. Science 378, 6624 (2022), 1092–1097. find our card
  152. Zongjie Li, Pingchuan Ma, Huaijin Wang, Shuai Wang, Qiyi Tang, Sen Nie, and Shi Wu. 2022. Unleashing the power of compiler intermediate representation to enhance neural program embeddings. In Proceedings of the 44th International Conference on Software Engineering. 2253–2265. find
  153. Vladislav Lialin, Vijeta Deshpande, and Anna Rumshisky. 2023. Scaling down to scale up: A guide to parameter-efficient fine-tuning. arXiv preprint arXiv:2303.15647 (2023). find
  154. Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. 2023. Holistic Evaluation of Language Models. Transactions on Machine Learning Research (2023). find
  155. Andreas Liesenfeld, Alianda Lopez, and Mark Dingemanse. 2023. Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators. In Proceedings of the 5th international conference on conversational user interfaces. 1–6. find
  156. Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out. 74–81. find
  157. Tianyang Lin, Yuxin Wang, Xiangyang Liu, and Xipeng Qiu. 2022. A survey of transformers. AI open 3 (2022), 111–132. find
  158. Erik Linstead, Paul Rigor, Sushil Bajracharya, Cristina Lopes, and Pierre Baldi. 2007. Mining internet-scale software repositories. Advances in neural information processing systems 20 (2007). find
  159. Aixin Liu, Bei Feng, Bin Wang, Bingxuan Wang, Bo Liu, Chenggang Zhao, Chengqi Dengr, Chong Ruan, Damai Dai, Daya Guo, et al. 2024. Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model. arXiv preprint arXiv:2405.04434 (2024). find
  160. Bingchang Liu, Chaoyu Chen, Cong Liao, Zi Gong, Huan Wang, Zhichao Lei, Ming Liang, Dajun Chen, Min Shen, Hailian Zhou, et al. 2023. Mftcoder: Boosting code llms with multitask fine-tuning. arXiv preprint arXiv:2311.02303 (2023). — [arXiv:2311.02303](https://arxiv.org/abs/2311.02303) find
  161. Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel. 2022. Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. Advances in Neural Information Processing Systems 35 (2022), 1950–1965. find our card
  162. Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. 2024. Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation. Advances in Neural Information Processing Systems 36 (2024). find our card
  163. Jiate Liu, Yiqin Zhu, Kaiwen Xiao, Qiang Fu, Xiao Han, Wei Yang, and Deheng Ye. 2023. Rltf: Reinforcement learning from unit test feedback. arXiv preprint arXiv:2307.04349 (2023). find
  164. Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys 55, 9 (2023), 1–35. find our card
  165. Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang, Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang, Denny Zhou, et al. 2024. Best Practices and Lessons Learned on Synthetic Data for Language Models. arXiv preprint arXiv:2404.07503 (2024). find
  166. Shangqing Liu, Yu Chen, Xiaofei Xie, Jing Kai Siow, and Yang Liu. 2020. Retrieval-Augmented Generation for Code Summarization via Hybrid GNN. In International Conference on Learning Representations. find
  167. Tianyang Liu, Canwen Xu, and Julian McAuley. 2023. Repobench: Benchmarking repository-level code auto-completion systems. arXiv preprint arXiv:2306.03091 (2023). find
  168. Yan Liu, Xiaokang Chen, Yan Gao, Zhe Su, Fengji Zhang, Daoguang Zan, Jian-Guang Lou, Pin-Yu Chen, and Tsung-Yi Ho. 2023. Uncovering and quantifying social biases in code generation. Advances in Neural Information Processing Systems 36 (2023), 2368–2380. find
  169. Yue Liu, Chakkrit Tantithamthavorn, Li Li, and Yepang Liu. 2022. Deep learning for android malware defenses: a systematic literature review. Comput. Surveys 55, 8 (2022), 1–36. find
  170. 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. StarCoder 2 and The Stack v2: The Next Generation. arXiv preprint arXiv:2402.19173 (2024). find
  171. Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, and Alexey Svyatkovskiy. 2022. ReACC: A Retrieval-Augmented Code Completion Framework. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 6227–6240. find
  172. Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al. 2021. Codexglue: A machine learning benchmark dataset for code understanding and generation. arXiv preprint arXiv:2102.04664 (2021). find
  173. Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2023. WizardCoder: Empowering Code Large Language Models with Evol-Instruct. In The Twelfth International Conference on Learning Representations. find
  174. Michael R Lyu, Baishakhi Ray, Abhik Roychoudhury, Shin Hwei Tan, and Patanamon Thongtanunam. 2024. Automatic Programming: Large Language Models and Beyond. arXiv preprint arXiv:2405.02213 (2024). — [arXiv:2405.02213](https://arxiv.org/abs/2405.02213) find
  175. Wei Ma, Mengjie Zhao, Xiaofei Xie, Qiang Hu, Shangqing Liu, Jie Zhang, Wenhan Wang, and Yang Liu. 2022. Are Code Pre-trained Models Powerful to Learn Code Syntax and Semantics? arXiv preprint arXiv:2212.10017 (2022). — [arXiv:2212.10017](https://arxiv.org/abs/2212.10017) find
  176. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2024. Self-refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems 36 (2024). find
  177. James Manyika and Sissie Hsiao. 2023. An overview of Bard: an early experiment with generative AI. AI. Google Static Documents 2 (2023). find
  178. Vadim Markovtsev, Waren Long, Hugo Mougard, Konstantin Slavnov, and Egor Bulychev. 2019. STYLE-ANALYZER: fixing code style inconsistencies with interpretable unsupervised algorithms. In 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR). IEEE, 468–478. find
  179. Yu Meng, Jiaxin Huang, Yu Zhang, and Jiawei Han. 2022. Generating training data with language models: Towards zero-shot language understanding. Advances in Neural Information Processing Systems 35 (2022), 462–477. find our card
  180. Meta. 2024. Introducing Meta Llama 3: The most capable openly available LLM to date. https://ai.meta.com/blog/meta-llama-3/. ↗
  181. MistralAI. 2024. Codestral. https://mistral.ai/news/codestral/. ↗
  182. Sébastien Bubeck Mojan Javaheripi. 2023. Phi-2: The surprising power of small language models. https://www. microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models. find
  183. Lili Mou, Ge Li, Zhi Jin, Lu Zhang, and Tao Wang. 2014. TBCNN: A tree-based convolutional neural network for programming language processing. arXiv preprint arXiv:1409.5718 (2014). find
  184. Zahra Mousavi, Chadni Islam, Kristen Moore, Alsharif Abuadbba, and M Ali Babar. 2024. An investigation into misuse of java security apis by large language models. In Proceedings of the 19th ACM Asia Conference on Computer and Communications Security. 1299–1315. find
  185. Spyridon Mouselinos, Mateusz Malinowski, and Henryk Michalewski. 2022. A simple, yet effective approach to finding biases in code generation. arXiv preprint arXiv:2211.00609 (2022). find
  186. Hussein Mozannar, Gagan Bansal, Adam Fourney, and Eric Horvitz. 2022. Reading between the lines: Modeling user behavior and costs in AI-assisted programming. arXiv preprint arXiv:2210.14306 (2022). — [arXiv:2210.14306](https://arxiv.org/abs/2210.14306) find
  187. Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro Von Werra, and Shayne Longpre. 2023. Octopack: Instruction tuning code large language models. arXiv preprint arXiv:2308.07124 (2023). find
  188. King Han Naman Jain, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. 2024. Livecodebench: Holistic and contamination free evaluation of large language models for code. arXiv preprint arXiv:2403.07974 (2024). find our card
  189. Antonio Nappa, Richard Johnson, Leyla Bilge, Juan Caballero, and Tudor Dumitras. 2015. The attack of the clones: A study of the impact of shared code on vulnerability patching. In 2015 IEEE symposium on security and privacy. IEEE, 692–708. find
  190. Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-tau Yih, Sida Wang, and Xi Victoria Lin. 2023. Lever: Learning to verify language-to-code generation with execution. In International Conference on Machine Learning. PMLR, 26106–26128. find our card
  191. Ansong Ni, Pengcheng Yin, Yilun Zhao, Martin Riddell, Troy Feng, Rui Shen, Stephen Yin, Ye Liu, Semih Yavuz, Caiming Xiong, et al. 2023. L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models. arXiv preprint arXiv:2309.17446 (2023). find
  192. Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, and Yingbo Zhou. 2023. Codegen2: Lessons for training llms on programming and natural languages. arXiv preprint arXiv:2305.02309 (2023). find
  193. Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022. Codegen: An open large language model for code with multi-turn program synthesis. arXiv preprint arXiv:2203.13474 (2022). find
  194. Changan Niu, Chuanyi Li, Bin Luo, and Vincent Ng. 2022. Deep learning meets software engineering: A survey on pre-trained models of source code. arXiv preprint arXiv:2205.11739 (2022). — [arXiv:2205.11739](https://arxiv.org/abs/2205.11739) find
  195. Theo X Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, and Armando Solar-Lezama. 2023. Is Self-Repair a Silver Bullet for Code Generation?. In The Twelfth International Conference on Learning Representations. find
  196. OpenAI. 2022. Chatgpt: Optimizing language models for dialogue. https://openai.com/blog/chatgpt. ↗
  197. OpenAI. 2024. Hello GPT-4o. https://openai.com/index/hello-gpt-4o/. ↗
  198. OpenAI. 2024. New models and developer products announced at DevDay. https://openai.com/index/new-models-and-developer-products-announced-at-devday/. ↗
  199. OpenDevin. 2024. OpenDevin: Code Less, Make More. https://github.com/OpenDevin/OpenDevin. ↗
  200. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems 35 (2022), 27730–27744. find
  201. Oded Ovadia, Menachem Brief, Moshik Mishaeli, and Oren Elisha. 2023. Fine-tuning or retrieval? comparing knowledge injection in llms. arXiv preprint arXiv:2312.05934 (2023). find
  202. David N Palacio, Alejandro Velasco, Daniel Rodriguez-Cardenas, Kevin Moran, and Denys Poshyvanyk. 2023. Evaluating and explaining large language models for code using syntactic structures. arXiv preprint arXiv:2308.03873 (2023). — [arXiv:2308.03873](https://arxiv.org/abs/2308.03873) find
  203. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 311–318. find our card
  204. Nikhil Parasaram, Huijie Yan, Boyu Yang, Zineb Flahy, Abriele Qudsi, Damian Ziaber, Earl Barr, and Sergey Mechtaev. 2024. The Fact Selection Problem in LLM-Based Program Repair. arXiv preprint arXiv:2404.05520 (2024). — [arXiv:2404.05520](https://arxiv.org/abs/2404.05520) find
  205. Md Rizwan Parvez, Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021. Retrieval Augmented Code Generation and Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2021. 2719–2734. find
  206. Arkil Patel, Siva Reddy, Dzmitry Bahdanau, and Pradeep Dasigi. 2023. Evaluating In-Context Learning of Libraries for Code Generation. arXiv preprint arXiv:2311.09635 (2023). find
  207. Indraneil Paul, Jun Luo, Goran Glavaš, and Iryna Gurevych. 2024. IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators. arXiv preprint arXiv:2403.03894 (2024). find
  208. Norman Peitek, Sven Apel, Chris Parnin, André Brechmann, and Janet Siegmund. 2021. Program comprehension and code complexity metrics: An fmri study. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). IEEE, 524–536. find
  209. Huy N Phan, Hoang N Phan, Tien N Nguyen, and Nghi DQ Bui. 2024. RepoHyper: Better Context Retrieval Is All You Need for Repository-Level Code Completion. arXiv preprint arXiv:2403.06095 (2024). — [arXiv:2403.06095](https://arxiv.org/abs/2403.06095) find
  210. Nikhil Pinnaparaju, Reshinth Adithyan, Duy Phung, Jonathan Tow, James Baicoianu, Ashish Datta, Maksym Zhuravinskyi, Dakota Mahan, Marco Bellagente, Carlos Riquelme, et al. 2024. Stable Code Technical Report. arXiv preprint arXiv:2404.01226 (2024). find
  211. Ofir Press, Noah A Smith, and Mike Lewis. 2021. Train short, test long: Attention with linear biases enables input length extrapolation. arXiv preprint arXiv:2108.12409 (2021). find
  212. Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. 2023. Fine-tuning aligned language models compromises safety, even when users do not intend to! arXiv preprint arXiv:2310.03693 (2023). find our card
  213. Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018. Improving language understanding by generative pre-training. (2018). find
  214. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9. find
  215. Steven Raemaekers, Arie Van Deursen, and Joost Visser. 2012. Measuring software library stability through historical version analysis. In 2012 28th IEEE international conference on software maintenance (ICSM). IEEE, 378–387. find
  216. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems 36 (2024). find
  217. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research 21, 140 (2020), 1–67. find
  218. Nitarshan Rajkumar, Raymond Li, and Dzmitry Bahdanau. 2022. Evaluating the text-to-sql capabilities of large language models. arXiv preprint arXiv:2204.00498 (2022). — [arXiv:2204.00498](https://arxiv.org/abs/2204.00498) find
  219. Aurora Ramirez, Jose Raul Romero, and Christopher L Simons. 2018. A systematic review of interaction in search-based software engineering. IEEE Transactions on Software Engineering 45, 8 (2018), 760–781. find
  220. Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al. 2024. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.… find our card
  221. Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, and Shuai Ma. 2020. Codebleu: a method for automatic evaluation of code synthesis. arXiv preprint arXiv:2009.10297 (2020). — [arXiv:2009.10297](https://arxiv.org/abs/2009.10297) find
  222. Replit. 2016. Idea to software, fast. https://replit.com. ↗
  223. Replit. 2023. replit-code-v1-3b. https://huggingface.co/replit/replit-code-v1-3b. ↗
  224. Tal Ridnik, Dedy Kredo, and Itamar Friedman. 2024. Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering. arXiv preprint arXiv:2401.08500 (2024). find
  225. Nick Roshdieh. 2023. Evol-Instruct-Code-80k. https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1. ↗
  226. Steven I Ross, Fernando Martinez, Stephanie Houde, Michael Muller, and Justin D Weisz. 2023. The programmer’s assistant: Conversational interaction with a large language model for software development. In Proceedings of the 28th International Conference on Intelligent User Interfaces. 491–514. find
  227. Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al. 2023. Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950 (2023). find our card
  228. Siddharth Samsi, Dan Zhao, Joseph McDonald, Baolin Li, Adam Michaleas, Michael Jones, William Bergeron, Jeremy Kepner, Devesh Tiwari, and Vijay Gadepally. 2023. From words to watts: Benchmarking the energy costs of large language model inference. In 2023 IEEE High Performance Extreme Computing Confe… find
  229. Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2022. Multitask Prompted Training Enables Zero-Shot Task Generalization. In ICLR 2022-Tenth International Conference on Learning Representatio… find our card
  230. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017). find
  231. Roei Schuster, Congzheng Song, Eran Tromer, and Vitaly Shmatikov. 2021. You autocomplete me: Poisoning vulnerabilities in neural code completion. In 30th USENIX Security Symposium (USENIX Security 21). 1559–1575. find
  232. Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. 2018. Self-attention with relative position representations. arXiv preprint arXiv:1803.02155 (2018). find our card
  233. Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538 (2017). find
  234. Bo Shen, Jiaxin Zhang, Taihong Chen, Daoguang Zan, Bing Geng, An Fu, Muhan Zeng, Ailun Yu, Jichuan Ji, Jingyang Zhao, et al. 2023. Pangu-coder2: Boosting large language models for code with ranking feedback. arXiv preprint arXiv:2307.14936 (2023). — [arXiv:2307.14936](https://arxiv.org/abs/2307.14936) find
  235. Jieke Shi, Zhou Yang, Hong Jin Kang, Bowen Xu, Junda He, and David Lo. 2024. Greening large language models of code. In Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Society. 142–153. find
  236. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2024. Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems 36 (2024). find our card
  237. Atsushi Shirafuji, Yusuke Oda, Jun Suzuki, Makoto Morishita, and Yutaka Watanobe. 2023. Refactoring Programs Using Large Language Models with Few-Shot Examples. arXiv preprint arXiv:2311.11690 (2023). — [arXiv:2311.11690](https://arxiv.org/abs/2311.11690) find
  238. Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, and Chandan K Reddy. 2023. Execution-based code generation using deep reinforcement learning. arXiv preprint arXiv:2301.13816 (2023). find
  239. Disha Shrivastava, Denis Kocetkov, Harm de Vries, Dzmitry Bahdanau, and Torsten Scholak. 2023. RepoFusion: Training Code Models to Understand Your Repository. arXiv preprint arXiv:2306.10998 (2023). — [arXiv:2306.10998](https://arxiv.org/abs/2306.10998) find
  240. Disha Shrivastava, Hugo Larochelle, and Daniel Tarlow. 2023. Repository-level prompt generation for large language models of code. In International Conference on Machine Learning. PMLR, 31693–31715. find
  241. Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le, Carina Negreanu, and Gust Verbruggen. 2023. Codefusion: A pre-trained diffusion model for code generation. arXiv preprint arXiv:2310.17680 (2023). find
  242. Hongjin Su, Shuyang Jiang, Yuhang Lai, Haoyuan Wu, Boao Shi, Che Liu, Qian Liu, and Tao Yu. 2024. ARKS: Active Retrieval in Knowledge Soup for Code Generation. arXiv preprint arXiv:2402.12317 (2024). — [arXiv:2402.12317](https://arxiv.org/abs/2402.12317) find
  243. Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. 2024. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing 568 (2024), 127063. find
  244. Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan. 2020. Intellicode compose: Code generation using transformer. In Proceedings of the 28th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering. 1433–1443. find
  245. Marc Szafraniec, Baptiste Roziere, Hugh Leather, Francois Charton, Patrick Labatut, and Gabriel Synnaeve. 2022. Code translation with compiler representations. In Proceedings of the Eleventh International Conference on Learning Representations: ICLR. find
  246. TabNine. 2018. AI Code Completions. https://github.com/codota/TabNine. ↗
  247. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford Alpaca: An Instruction-following LLaMA model. https://github.com/tatsu-lab/stanford\_alpaca. ↗
  248. Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al. 2024. Gemma: Open models based on gemini research and technology. arXiv preprint arXiv:2403.08295 (2024). find
  249. Qwen Team. 2024. Code with CodeQwen1.5. https://qwenlm.github.io/blog/codeqwen1.5. ↗
  250. Shailja Thakur, Baleegh Ahmad, Zhenxing Fan, Hammond Pearce, Benjamin Tan, Ramesh Karri, Brendan Dolan-Gavitt, and Siddharth Garg. 2023. Benchmarking large language models for automated verilog rtl code generation. In 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1–6… find
  251. theblackcat102. 2023. The evolved code alpaca dataset. https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1. ↗
  252. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023). find
  253. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). find
  254. Lewis Tunstall, Leandro Von Werra, and Thomas Wolf. 2022. Natural language processing with transformers. " O’Reilly Media, Inc.". find
  255. Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. 2022. Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Chi conference on human factors in computing systems extended abstracts. 1–7. find
  256. Boris Van Breugel, Zhaozhi Qian, and Mihaela Van Der Schaar. 2023. Synthetic data, real errors: how (not) to publish and use synthetic data. In International Conference on Machine Learning. PMLR, 34793–34808. find
  257. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017). find
  258. Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https: //github.com/kingoflolz/mesh-transformer-jax. find
  259. Chong Wang, Jian Zhang, Yebo Feng, Tianlin Li, Weisong Sun, Yang Liu, and Xin Peng. 2024. Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation. arXiv preprint arXiv:2401.06391 (2024). — [arXiv:2401.06391](https://arxiv.org/abs/2401.06391) find
  260. Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, and Qing Wang. 2024. Software testing with large language models: Survey, landscape, and vision. IEEE Transactions on Software Engineering (2024). find
  261. Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, et al. 2024. A survey on large language model based autonomous agents. Frontiers of Computer Science 18, 6 (2024), 1–26. find our card
  262. Simin Wang, Liguo Huang, Amiao Gao, Jidong Ge, Tengfei Zhang, Haitao Feng, Ishna Satyarth, Ming Li, He Zhang, and Vincent Ng. 2022. Machine/deep learning for software engineering: A systematic literature review. IEEE Transactions on Software Engineering 49, 3 (2022), 1188–1231. find
  263. Shiqi Wang, Li Zheng, Haifeng Qian, Chenghao Yang, Zijian Wang, Varun Kumar, Mingyue Shang, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, and Bing Xiang. 2022. ReCode: Robustness Evaluation of Code Generation Models. (2022). https://doi.org/10.48… find
  264. Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, et al. 2023. Knowledge editing for large language models: A survey. arXiv preprint arXiv:2310.16218 (2023). find
  265. Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji. 2024. Executable code actions elicit better llm agents. arXiv preprint arXiv:2402.01030 (2024). find our card
  266. Xin Wang, Yasheng Wang, Yao Wan, Fei Mi, Yitong Li, Pingyi Zhou, Jin Liu, Hao Wu, Xin Jiang, and Qun Liu. 2022. Compilable Neural Code Generation with Compiler Feedback. In Findings of the Association for Computational Linguistics: ACL 2022. 9–19. find
  267. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171 (2022). find our card
  268. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. Self-Instruct: Aligning Language Models with Self-Generated Instructions. In The 61st Annual Meeting Of The Association For Computational Linguistics. find
  269. Yue Wang, Hung Le, Akhilesh Gotmare, Nghi Bui, Junnan Li, and Steven Hoi. 2023. CodeT5+: Open Code Large Language Models for Code Understanding and Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 1069–1088. find
  270. Yanlin Wang and Hui Li. 2021. Code completion by modeling flattened abstract syntax trees as graphs. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 14015–14023. find
  271. Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021. 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. find
  272. Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, and Qun Liu. 2023. Aligning large language models with human: A survey. arXiv preprint arXiv:2307.12966 (2023). find our card
  273. Zhiruo Wang, Shuyan Zhou, Daniel Fried, and Graham Neubig. 2022. Execution-based evaluation for open-domain code generation. arXiv preprint arXiv:2212.10481 (2022). find
  274. Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021. Finetuned language models are zero-shot learners. arXiv preprint arXiv:2109.01652 (2021). find
  275. Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022. Emergent Abilities of Large Language Models. Transactions on Machine Learning Research (2022). find our card
  276. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems 35 (2022), 24824–24837. find our card
  277. Xiaokai Wei, Sujan Kumar Gonugondla, Shiqi Wang, Wasi Ahmad, Baishakhi Ray, Haifeng Qian, Xiaopeng Li, Varun Kumar, Zijian Wang, Yuchen Tian, et al. 2023. Towards greener yet powerful code generation via quantization: An empirical study. In Proceedings of the 31st ACM Joint European Software Enginee… find
  278. Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, and Lingming Zhang. 2023. Magicoder: Source code is all you need. arXiv preprint arXiv:2312.02120 (2023). find
  279. Lilian Weng. 2023. LLM-powered Autonomous Agents. lilianweng.github.io (Jun 2023). https://lilianweng.github.io/posts/2023-06-23-agent/ ↗
  280. Alexander Wettig, Aatmik Gupta, Saumya Malik, and Danqi Chen. 2024. QuRating: Selecting High-Quality Data for Training Language Models. arXiv preprint arXiv:2402.09739 (2024). find
  281. Erroll Wood, Tadas Baltrušaitis, Charlie Hewitt, Sebastian Dziadzio, Thomas J Cashman, and Jamie Shotton. 2021. Fake it till you make it: face analysis in the wild using synthetic data alone. In Proceedings of the IEEE/CVF international conference on computer vision. 3681–3691. find
  282. Di Wu, Wasi Uddin Ahmad, Dejiao Zhang, Murali Krishna Ramanathan, and Xiaofei Ma. 2024. Repoformer: Selective Retrieval for Repository-Level Code Completion. arXiv preprint arXiv:2403.10059 (2024). — [arXiv:2403.10059](https://arxiv.org/abs/2403.10059) find
  283. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023. Autogen: Enabling next-gen llm applications via multi-agent conversation framework. arXiv preprint arXiv:2308.08155 (2023). find our card
  284. Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, et al. 2023. The rise and potential of large language model based agents: A survey. arXiv preprint arXiv:2309.07864 (2023). find our card
  285. Rui Xie, Zhengran Zeng, Zhuohao Yu, Chang Gao, Shikun Zhang, and Wei Ye. 2024. CodeShell Technical Report. arXiv preprint arXiv:2403.15747 (2024). — [arXiv:2403.15747](https://arxiv.org/abs/2403.15747) find
  286. Sang Michael Xie, Shibani Santurkar, Tengyu Ma, and Percy S Liang. 2023. Data selection for language models via importance resampling. Advances in Neural Information Processing Systems 36 (2023), 34201–34227. find
  287. Bowen Li Xingyao Wang and Graham Neubig. 2024. Introducing OpenDevin CodeAct 1.0, a new State-of-the-art in Coding Agents. https://www.cognition.ai/introducing-devin. ↗
  288. Yingfei Xiong, Jie Wang, Runfa Yan, Jiachen Zhang, Shi Han, Gang Huang, and Lu Zhang. 2017. Precise condition synthesis for program repair. In 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE). IEEE, 416–426. find
  289. Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023. Wizardlm: Empowering large language models to follow complex instructions. arXiv preprint arXiv:2304.12244 (2023). find our card
  290. Frank F Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn. 2022. A systematic evaluation of large language models of code. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming. 1–10. find
  291. Junjielong Xu, Ying Fu, Shin Hwei Tan, and Pinjia He. 2024. Aligning LLMs for FL-free Program Repair. arXiv preprint arXiv:2404.08877 (2024). — [arXiv:2404.08877](https://arxiv.org/abs/2404.08877) find
  292. Weiwei Xu, Kai Gao, Hao He, and Minghui Zhou. 2024. A First Look at License Compliance Capability of LLMs in Code Generation. arXiv preprint arXiv:2408.02487 (2024). — [arXiv:2408.02487](https://arxiv.org/abs/2408.02487) find
  293. Zhou Yang, Zhensu Sun, Terry Zhuo Yue, Premkumar Devanbu, and David Lo. 2024. Robustness, security, privacy, explainability, efficiency, and usability of large language models for code. arXiv preprint arXiv:2403.07506 (2024). — [arXiv:2403.07506](https://arxiv.org/abs/2403.07506) find
  294. Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsun Kim, Donggyun Han, and David Lo. 2024. Unveiling memorization in code models. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering. 1–13. find
  295. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024. Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems 36 (2024). find
  296. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023. ReAct: Synergizing Reasoning and Acting in Language Models. In International Conference on Learning Representations (ICLR). find our card
  297. Pengcheng Yin, Bowen Deng, Edgar Chen, Bogdan Vasilescu, and Graham Neubig. 2018. Learning to mine aligned code and natural language pairs from stack overflow. In Proceedings of the 15th international conference on mining software repositories. 476–486. find our card
  298. Kang Min Yoo, Jaegeun Han, Sookyo In, Heewon Jeon, Jisu Jeong, Jaewook Kang, Hyunwook Kim, Kyung-Min Kim, Munhyong Kim, Sungju Kim, et al. 2024. HyperCLOVA X Technical Report. arXiv preprint arXiv:2404.01954 (2024). find
  299. Hao Yu, Bo Shen, Dezhi Ran, Jiaxin Zhang, Qi Zhang, Yuchi Ma, Guangtai Liang, Ying Li, Qianxiang Wang, and Tao Xie. 2024. Codereval: A benchmark of pragmatic code generation with generative pre-trained models. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering. 1–12… find
  300. Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al. 2018. Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task. In Proceedings of the 2018 Conference on Empirical Me… find our card
  301. Zhaojian Yu, Xin Zhang, Ning Shang, Yangyu Huang, Can Xu, Yishujie Zhao, Wenxiang Hu, and Qiufeng Yin. 2023. Wavecoder: Widespread and versatile enhanced instruction tuning with refined data generation. arXiv preprint arXiv:2312.14187 (2023). — [arXiv:2312.14187](https://arxiv.org/abs/2312.14187) find
  302. Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Pinjia He, Shuming Shi, and Zhaopeng Tu. 2023. Gpt-4 is too smart to be safe: Stealthy chat with llms via cipher. arXiv preprint arXiv:2308.06463 (2023). find our card
  303. Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang. 2023. Rrhf: Rank responses to align language models with human feedback without tears. arXiv preprint arXiv:2304.05302 (2023). find our card
  304. Jiawei Liu Yifeng Ding Naman Jain Harm de Vries Leandro von Werra Arjun Guha Lingming Zhang Yuxiang Wei, Federico Cassano. 2024. StarCoder2-Instruct: Fully Transparent and Permissive Self-Alignment for Code Generation. https://github.com/bigcode-project/starcoder2-self-align. ↗
  305. Elad Ben Zaken, Shauli Ravfogel, and Yoav Goldberg. 2021. Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. arXiv preprint arXiv:2106.10199 (2021). find our card
  306. Daoguang Zan, Bei Chen, Dejian Yang, Zeqi Lin, Minsu Kim, Bei Guan, Yongji Wang, Weizhu Chen, and Jian-Guang Lou. 2022. CERT: continual pre-training on sketches for library-oriented code generation. arXiv preprint arXiv:2206.06888 (2022). — [arXiv:2206.06888](https://arxiv.org/abs/2206.06888) find
  307. Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, and Jian-Guang Lou. 2023. Large Language Models Meet NL2Code: A Survey. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 7443–7464. find
  308. Daoguang Zan, Ailun Yu, Wei Liu, Dong Chen, Bo Shen, Wei Li, Yafen Yao, Yongshun Gong, Xiaolin Chen, Bei Guan, et al. 2024. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. arXiv preprint arXiv:2403.16443 (2024). find
  309. Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, and Weizhu Chen. 2023. RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 247… find
  310. Jialu Zhang, José Pablo Cambronero, Sumit Gulwani, Vu Le, Ruzica Piskac, Gustavo Soares, and Gust Verbruggen. 2024. Pydex: Repairing bugs in introductory python assignments using llms. Proceedings of the ACM on Programming Languages 8, OOPSLA1 (2024), 1100–1124. find
  311. Jialu Zhang, De Li, John Charles Kolesar, Hanyuan Shi, and Ruzica Piskac. 2022. Automated feedback generation for competition-level code. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering. 1–13. find
  312. Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao. 2023. Adaptive budget allocation for parameter-efficient fine-tuning. In The Eleventh International Conference on Learning Representations. find
  313. Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, et al. 2023. Instruction tuning for large language models: A survey. arXiv preprint arXiv:2308.10792 (2023). find our card
  314. Shudan Zhang, Hanlin Zhao, Xiao Liu, Qinkai Zheng, Zehan Qi, Xiaotao Gu, Xiaohan Zhang, Yuxiao Dong, and Jie Tang. 2024. NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Prompts. arXiv preprint arXiv:2405.04520 (2024). — [arXiv:2405.04520](https://arxiv.org/abs/2405.04520) find
  315. Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al. 2023. Siren’s song in the AI ocean: a survey on hallucination in large language models. arXiv preprint arXiv:2309.01219 (2023). find our card
  316. Yuntong Zhang, Haifeng Ruan, Zhiyu Fan, and Abhik Roychoudhury. 2024. AutoCodeRover: Autonomous Program Improvement. arXiv preprint arXiv:2404.05427 (2024). — [arXiv:2404.05427](https://arxiv.org/abs/2404.05427) find
  317. Ziyin Zhang, Chaoyu Chen, Bingchang Liu, Cong Liao, Zi Gong, Hang Yu, Jianguo Li, and Rui Wang. 2023. Unifying the perspectives of nlp and software engineering: A survey on language models for code. arXiv preprint arXiv:2311.07989 (2023). — [arXiv:2311.07989](https://arxiv.org/abs/2311.07989) find
  318. Liang Zhao, Xiaocheng Feng, Xiachong Feng, Bin Qin, and Ting Liu. 2023. Length Extrapolation of Transformers: A Survey from the Perspective of Position Encoding. arXiv preprint arXiv:2312.17044 (2023). — [arXiv:2312.17044](https://arxiv.org/abs/2312.17044) find
  319. Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023. A survey of large language models. arXiv preprint arXiv:2303.18223 (2023). find our card
  320. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2024. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems 36 (2024). find
  321. Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Lei Shen, Zihan Wang, Andi Wang, Yang Li, et al. 2023. Codegeex: A pre-trained model for code generation with multilingual benchmarking on humaneval-x. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data… find our card
  322. Tianyu Zheng, Ge Zhang, Tianhao Shen, Xueling Liu, Bill Yuchen Lin, Jie Fu, Wenhu Chen, and Xiang Yue. 2024. OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement. arXiv preprint arXiv:2402.14658 (2024). find
  323. Wenqing Zheng, SP Sharan, Ajay Kumar Jaiswal, Kevin Wang, Yihan Xi, Dejia Xu, and Zhangyang Wang. 2023. Outline, then details: Syntactically guided coarse-to-fine code generation. In International Conference on Machine Learning. PMLR, 42403–42419. find
  324. Zibin Zheng, Kaiwen Ning, Yanlin Wang, Jingwen Zhang, Dewu Zheng, Mingxi Ye, and Jiachi Chen. 2023. A survey of large language models for code: Evolution, benchmarking, and future trends. arXiv preprint arXiv:2311.10372 (2023). — [arXiv:2311.10372](https://arxiv.org/abs/2311.10372) find
  325. Li Zhong, Zilong Wang, and Jingbo Shang. 2024. LDB: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step. arXiv preprint arXiv:2402.16906 (2024). find
  326. Aojun Zhou, Ke Wang, Zimu Lu, Weikang Shi, Sichun Luo, Zipeng Qin, Shaoqing Lu, Anya Jia, Linqi Song, Mingjie Zhan, et al. 2023. Solving challenging math word problems using gpt-4 code interpreter with code-based self-verification. arXiv preprint arXiv:2308.07921 (2023). find our card
  327. Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang. 2023. Language agent tree search unifies reasoning acting and planning in language models. arXiv preprint arXiv:2310.04406 (2023). find
  328. Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, et al. 2024. Lima: Less is more for alignment. Advances in Neural Information Processing Systems 36 (2024). find
  329. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, et al. 2022. Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. In The Eleventh International Conference on Learning Representations. find our card
  330. Shuyan Zhou, Uri Alon, Frank F Xu, Zhengbao Jiang, and Graham Neubig. 2022. DocPrompting: Generating Code by Retrieving the Docs. In The Eleventh International Conference on Learning Representations. find
  331. Qihao Zhu, Daya Guo, Zhihong Shao, Dejian Yang, Peiyi Wang, Runxin Xu, Y Wu, Yukun Li, Huazuo Gao, Shirong Ma, et al. 2024. DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence. arXiv preprint arXiv:2406.11931 (2024). find our card
  332. Terry Yue Zhuo. 2024. ICE-Score: Instructing Large Language Models to Evaluate Code. In Findings of the Association for Computational Linguistics: EACL 2024. 2232–2242. find
  333. Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, et al. 2024. Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions. arXiv preprint arXiv:2406.15877 (2024). find our card
  334. Terry Yue Zhuo, Armel Zebaze, Nitchakarn Suppattarachai, Leandro von Werra, Harm de Vries, Qian Liu, and Niklas Muennighoff. 2024. Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models. arXiv preprint arXiv:2401.00788 (2024). find