A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinement

Theoretical and Empirical Studies of Program Testing

William E. Howden. 1978 · 1978

Earlier work this paper cites.

Integrating pair programming into a software development process. In 14th Conference on Software Engineering Education and Training. ‘In search of a software engineering profession’ (Cat. No. PR01059) . IEEE, Charlotte, NC, USA, 27–36

L. Williams. 2001 · 2001

Earlier work this paper cites.

k-means++: The Advantages of Careful Seeding. In SODA . SIAM, New Orleans, LA, USA, 1027–1035

David Arthur and Sergei Vassilvitskii. 2007 · 2007

Earlier work this paper cites.

The Waterfall Model in Large-Scale Development. In PROFES . Springer, Oulu, Finland, 386–400

Kai Petersen, Claes Wohlin, and Dejan Baca. 2009 · 2009

Earlier work this paper cites.

The Curious Case of Neural Text Degeneration. In ICLR . OpenReview.net, Addis Ababa, Ethiopia, 1–16

Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020

Earlier work this paper cites.

Program Synthesis with Large Language Models

Jacob Austin, Augustus Odena, Maxwell I. Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, and Charles Sutton. 2021 · 2021

Earlier work this paper cites.

Evaluating Large Language Models Trained on Code

Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021 · 2021

Earlier work this paper cites.

Fault-Aware Neural Code Rankers. In NeurIPS . Curran Associates, Inc., New Orleans, LA, USA, 13419–13432

Jeevana Priya Inala, Chenglong Wang, Mei Yang, Andrés Codas, Mark Encarnación, Shuvendu K. Lahiri, Madanlal Musuvathi, and Jianfeng Gao. 2022 · 2022

Earlier work this paper cites.

Large Language Models are Zero-Shot Reasoners. In NeurIPS . Curran Associates, Inc., New Orleans, LA, USA, 22199–22213

Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022

Earlier work this paper cites.

Competition-Level Code Generation with AlphaCode

Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals. 2022 · 2022

Earlier work this paper cites.

Natural Language to Code Translation with Execution. In EMNLP . ACL, Abu Dhabi, United Arab Emirates, 3533–3546

Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, and Sida I. Wang. 2022 · 2022

Earlier work this paper cites.

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In NeurIPS . Curran Associates, Inc., New Orleans, LA, USA, 24824–24837

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022 · 2022

Earlier work this paper cites.

Multi-lingual Evaluation of Code Generation Models. In ICLR . OpenReview.net, Kigali, Rwanda, 1–19

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, and Ramesh Nallapati. 2023 · 2023

Earlier work this paper cites.

CodeT: Code Generation with Generated Tests. In ICLR . OpenReview.net, Kigali, Rwanda, 1–19

Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2023 · 2023

Earlier work this paper cites.

Batch Prompting: Efficient Inference with Large Language Model APIs. In EMNLP . ACL, Singapore, 792–810

Zhoujun Cheng, Jungo Kasai, and Tao Yu. 2023 · 2023

Earlier work this paper cites.

Self-collaboration Code Generation via ChatGPT

Yihong Dong, Xue Jiang, Zhi Jin, and Ge Li. 2023 · 2023

Earlier work this paper cites.

InCoder: A Generative Model for Code Infilling and Synthesis. In ICLR . OpenReview.net, Kigali, Rwanda, 1–26

Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, and Mike Lewis. 2023 · 2023

Earlier work this paper cites.

What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs?. In ASE . IEEE, Kirchberg, Luxembourg, 761–773

Shuzheng Gao, Xin-Cheng Wen, Cuiyun Gao, Wenxuan Wang, Hongyu Zhang, and Michael R. Lyu. 2023 · 2023

Cited alongside, same era.

CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation

Dong Huang, Qingwen Bu, Yuhao Qing, and Heming Cui. 2023 · 2023

Cited alongside, same era.

Self-planning Code Generation with Large Language Model

Xue Jiang, Yihong Dong, Lecheng Wang, Qiwei Shang, and Ge Li. 2023 · 2023

Cited alongside, same era.

Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming. In CHI . ACM, Hamburg, Germany, 455:1–455:23

Majeed Kazemitabaar, Justin Chow, Carl Ka To Ma, Barbara J. Ericson, David Weintrop, and Tovi Grossman. 2023 · 2023

Cited alongside, same era.

Structured Chain-of-Thought Prompting for Code Generation

Jia Li, Ge Li, Yongmin Li, and Zhi Jin. 2023a · 2023

Cited alongside, same era.

Meet Claude

Anthropic. 2023 · 2024

Closest in time.

Teaching Large Language Models to Self-Debug. In ICLR . OpenReview.net, Vienna, Austria, 1–80

Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2024 · 2024

Closest in time.

Large Language Models are Edge-Case Generators: Crafting Unusual Programs for Fuzzing Deep Learning Libraries. In ICSE . IEEE/ACM, Lisbon, Portugal, 70:1–70:13

Yinlin Deng, Chunqiu Steven Xia, Chenyuan Yang, Shizhuo Dylan Zhang, Shujing Yang, and Lingming Zhang. 2024 · 2024

Closest in time.

Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning. In ICSE . IEEE/ACM, Lisbon, Portugal, 39:1–39:13

Mingyang Geng, Shangwen Wang, Dezun Dong, Haotian Wang, Ge Li, Zhi Jin, Xiaoguang Mao, and Xiangke Liao. 2024 · 2024

Closest in time.

DeepSeek-Coder: When the Large Language Model Meets Programming - The Rise of Code Intelligence

Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y. K. Li, Fuli Luo, Yingfei Xiong, and Wenfeng Liang. 2024 · 2024