Fetching the paper…
Reading the bibliography…
High-quality code documentation is crucial for software development especially in the era of AI.
Depth-first search and linear graph algorithms
Robert Tarjan. 1972 · 1972
Earlier work this paper cites.
A study of the documentation essential to software maintenance
Sergio Cozzetti B De Souza, Nicolas Anquetil, and Káthia M de Oliveira. 2005 · 2005
Earlier work this paper cites.
An empirical analysis of the impact of software development problem factors on software maintainability
Jie-Cherng Chen and Sun-Jen Huang. 2009 · 2009
Earlier work this paper cites.
What makes apis hard to learn? answers from developers
Martin P Robillard. 2009 · 2009
Earlier work this paper cites.
Precise documentation: The key to better software
David Lorge Parnas. 2010 · 2010
Earlier work this paper cites.
Usage and usefulness of technical software documentation: An industrial case study
Golara Garousi, Vahid Garousi-Yusifoğlu, Guenther Ruhe, Junji Zhi, Mahmoud Moussavi, and Brian Smith. 2015 · 2015
Earlier work this paper cites.
Towards prioritizing documentation effort
Paul W McBurney, Siyuan Jiang, Marouane Kessentini, Nicholas A Kraft, Ameer Armaly, Mohamed Wiem Mkaouer, and Collin McMillan. 2017 · 2017
Earlier work this paper cites.
Software documentation issues unveiled
Emad Aghajani, Csaba Nagy, Olga Lucero Vega-Márquez, Mario Linares-Vásquez, Laura Moreno, Gabriele Bavota, and Michele Lanza. 2019 · 2019
Earlier work this paper cites.
Pymt5: Multi-mode translation of natural language and python code with transformers
Colin B Clement, Andrew Terrell, Hanlin Mao, Joshua Dillon, Sameer Singh, and Dan Alistarh. 2020 · 2020
Earlier work this paper cites.
Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, and Daxin Jiang. 2020 · 2020
Earlier work this paper cites.
Unified pre-training for program understanding and generation
Wasi U Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Earlier work this paper cites.
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, and 1 others. 2021 · 2021
Earlier work this paper cites.
Graphcodebert: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Nan Duan, Ming Zhou, and Daxin Jiang. 2021 · 2021
Earlier work this paper cites.
Reassessing automatic evaluation metrics for code summarization tasks
Rahul Roy, Saikat Chakraborty, Baishakhi Ray, and Miryung Kim. 2021 · 2021
Earlier work this paper cites.
Automatic api usage scenario documentation from technical q&a sites
Gias Uddin, Foutse Khomh, and Chanchal K Roy. 2021 · 2021
Earlier work this paper cites.
Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Shuo Ren, Daya Lu, Duyu Tang, Nan Duan, Ming Zhou, and Daxin Jiang. 2021 · 2021
Earlier work this paper cites.
Efficient training of language models to fill in the middle
Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, and Mark Chen. 2022 · 2022
Cited alongside, same era.
React: Synergizing reasoning and acting in language models
Shinn Yao, Jeffrey Zhao, Dian Yu, Kang Chen, Karthik Narasimhan, and Yuan Cao. 2022 · 2022
Cited alongside, same era.
When language model meets private library
Yaqing Zan, Mingyu Ding, Bill Yuchen Lin, and Xiang Ren. 2022 · 2022
Cited alongside, same era.
Docprompting: Generating code by retrieving the docs
Shuyan Zhou, Uri Alon, Frank F Xu, Zhiruo Wang, Zhengbao Jiang, and Graham Neubig. 2022 · 2022
Cited alongside, same era.
Teaching large language models to self-debug
Qian Chen, Binyuan Tang, Yankai Zhang, Binhua Wang, Zhifang Zhang, and Qun Zhang. 2023 · 2023
A comparative analysis of large language models for code documentation generation
Shubhang Shekhar Dvivedi, Vyshnav Vijay, Sai Leela Rahul Pujari, Shoumik Lodh, and Dhruv Kumar. 2024 · 2024
Later among the works it cites.
Using large language models to document code: A first quantitative and qualitative assessment
Liron Guelman, Alon Lavie, and Eran Yahav. 2024 · 2024
Later among the works it cites.
Checkeval: A reliable llm-as-a-judge framework for evaluating text generation using checklists
Yukyung Lee, Wonjoon Cho, and Jinhyuk Kim. 2024 · 2024
Later among the works it cites.
Repoagent: An llm-powered open-source framework for repository-level code documentation generation
Qinyu Luo, Yining Ye, Shihao Liang, Zhong Zhang, Yujia Qin, Yaxi Lu, Yesai Wu, Xin Cong, Yankai Lin, Yingli Zhang, and 1 others. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Can large language models be an alternative to human evaluations?
Cheng-Han Chiang and Hung-yi Lee. 2023 · 2023
Cited alongside, same era.
Prometheus: Inducing fine-grained evaluation capability in language models
Seungone Kim, Soobin Kim, Alice Oh, and Gunhee Han. 2023 · 2023
Cited alongside, same era.
G-eval: Nlg evaluation using gpt-4 with better human alignment
Yang Liu, Yao Fu, Yujie Xie, Xinyi Chen, Bo Pang, Chenyan Qian, Teng Ma, and Dragomir Radev. 2023b · 2023
Cited alongside, same era.
Chatdev: Revolutionizing software development with ai-collaborative agents
Yuzhang Qian, Zian Zhang, Liang Pan, Peng Wang, Shouyi Liu, Wayne Xin Zhao, and Ji-Rong Wen. 2023 · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023 · 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, and 1 others. 2023 · 2023
Cited alongside, same era.
Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Margaret Labash, and Stefano Ermon. 2023 · 2023
Cited alongside, same era.
Yingwei Ma, Qingping Yang, Rongyu Cao, Binhua Li, Fei Huang, and Yongbin Li. 2024 · 2024
Later among the works it cites.
Autosurvey: Large language models can automatically write surveys
Yidong Wang, Qi Guo, Wenjin Yao, Hongbo Zhang, Xin Zhang, Zhen Wu, Meishan Zhang, Xinyu Dai, Qingsong Wen, Wei Ye, and 1 others. 2024 · 2024
Later among the works it cites.
Less is more: Docstring compression in code generation
Guang Yang, Yu Zhou, Wei Cheng, Xiangyu Zhang, Xiang Chen, Terry Yue Zhuo, Ke Liu, Xin Zhou, David Lo, and Taolue Chen. 2024 · 2024
Later among the works it cites.
Llm hallucinations in practical code generation: Phenomena, mechanism, and mitigation
Kaiyu Zhang, Yifei Wang, Yue Yu, Yujie Li, Zihan Lin, Dongxu Zhang, Yichi Zhou, Yifei Xu, Ang Chen, Weiyi Zhang, and 1 others. 2024 · 2024
Later among the works it cites.
How well do llms generate code for different application domains?
Zihan Zheng, Jiayi Zheng, Weiyan Liu, Yizhong Wang, Chen Liu, Xiang Lorraine Li, Mu Li, Wenhao Zhang, Diyi Huang, and Xiang Ren. 2024 · 2024
Later among the works it cites.
Model context length increases with the new context protocol
Anthropic. 2025 · 2025
Closest in time.
How github copilot is getting better at understanding your code
GitHub. 2024 · 2025
Closest in time.
No free labels: Limitations of llm-as-a-judge without human grounding
Michael Krumdick, Jason Wei, Xinyang Chen, Shangbin Du, Shu Xu, Dale Schuurmans, and Ed H Chi. 2025 · 2025
Closest in time.
Introducing chatgpt
OpenAI. 2022 · 2025
Closest in time.
Dayu Yang, Tianyang Liu, Daoan Zhang, and 1 others. 2025 · 2025
Closest in time.
Agent-as-a-judge: Evaluate agents with agents
Mingchen Zhuge, Changsheng Zhao, Dylan Ashley, Wenyi Wang, Dmitrii Khizbullin, Yunyang Xiong, Zechun Liu, Ernie Chang, Raghuraman Krishnamoorthi, Yuandong Tian, and 1 others. 2024 · 2025
Closest in time.