Fetching the paper…
Reading the bibliography…
This paper introduces DevGPT, a dataset curated to explore how software developers interact with ChatGPT, a prominent large language model (LLM).
A study of innovation diffusion through link sharing on stack overflow. In 2013 10th Working Conference on Mining Software Repositories (MSR) . IEEE, 81–84
Carlos Gómez, Brendan Cleary, and Leif Singer. 2013 · 2013
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
The structure and dynamics of knowledge network in domain-specific q&a sites: a case study of stack overflow
Deheng Ye, Zhenchang Xing, and Nachiket Kapre. 2017 · 2017
Earlier work this paper cites.
How developers document pull requests with external references. In 2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC) . IEEE, 23–33
Fiorella Zampetti, Luca Ponzanelli, Gabriele Bavota, Andrea Mocci, Massimiliano Di Penta, and Michele Lanza. 2017 · 2017
Earlier work this paper cites.
9.6 million links in source code comments: Purpose, evolution, and decay. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 1211–1221
Hideaki Hata, Christoph Treude, Raula Gaikovina Kula, and Takashi Ishio. 2019 · 2019
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, et al · 2021
Earlier work this paper cites.
Studying the usage of text-to-text transfer transformer to support code-related tasks. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 336–347
Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader Palacio, Denys Poshyvanyk, Rocco Oliveto, and Gabriele Bavota. 2021 · 2021
Earlier work this paper cites.
Understanding shared links and their intentions to meet information needs in modern code review: A case study of the OpenStack and Qt projects
Dong Wang, Tao Xiao, Patanamon Thongtanunam, Raula Gaikovina Kula, and Kenichi Matsumoto. 2021 · 2021
Earlier work this paper cites.
Code generation tools (almost) for free? a study of few-shot, pre-trained language models on code
Patrick Bareiß, Beatriz Souza, Marcelo d’Amorim, and Michael Pradel. 2022 · 2022
Earlier work this paper cites.
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
Fuxiang Chen, Fatemeh H Fard, David Lo, and Timofey Bryksin. 2022 · 2022
Earlier work this paper cites.
Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?
Jean-Baptiste Döderlein, Mathieu Acher, Djamel Eddine Khelladi, and Benoit Combemale. 2022 · 2022
Earlier work this paper cites.
Assemble foundation models for automatic code summarization. In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 935–946
Jian Gu, Pasquale Salza, and Harald C Gall. 2022 · 2022
Earlier work this paper cites.
Automatic detection and analysis of technical debts in peer-review documentation of r packages. In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 765–776
Junaed Younus Khan and Gias Uddin. 2022 · 2022
Earlier work this paper cites.
Using deep learning to generate complete log statements. In Proceedings of the 44th International Conference on Software Engineering . 2279–2290
Antonio Mastropaolo, Luca Pascarella, and Gabriele Bavota. 2022 · 2022
Earlier work this paper cites.
A systematic evaluation of large language models of code. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming . 1–10
Frank F Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn. 2022 · 2022
Cited alongside, same era.
An extensive study on pre-trained models for program understanding and generation. In Proceedings of the 31st ACM SIGSOFT international symposium on software testing and analysis . 39–51
Zhengran Zeng, Hanzhuo Tan, Haotian Zhang, Jing Li, Yuqun Zhang, and Lingming Zhang. 2022 · 2022
Cited alongside, same era.
Improving Few-Shot Prompts with Relevant Static Analysis Products
Toufique Ahmed, Kunal Suresh Pai, Premkumar Devanbu, and Earl T Barr. 2023 · 2023
Cited alongside, same era.
Exploring Distributional Shifts in Large Language Models for Code Analysis
Shushan Arakelyan, Rocktim Jyoti Das, Yi Mao, and Xiang Ren. 2023 · 2023
Cited alongside, same era.
Comparing Software Developers with ChatGPT: An Empirical Investigation
Nathalia Nascimento, Paulo Alencar, and Donald Cowan. 2023 · 2023
Closest in time.
Evaluating and improving transformers pre-trained on ASTs for Code Completion. In 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 834–844
Marcel Ochs, Krishna Narasimhan, and Mira Mezini. 2023 · 2023
Closest in time.
Examining zero-shot vulnerability repair with large language models. In 2023 IEEE Symposium on Security and Privacy (SP) . IEEE, 2339–2356
Hammond Pearce, Benjamin Tan, Baleegh Ahmad, Ramesh Karri, and Brendan Dolan-Gavitt. 2023 · 2023
Closest in time.
From Copilot to Pilot: Towards AI Supported Software Development
Rohith Pudari and Neil A Ernst. 2023 · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yihong Dong, Xue Jiang, Zhi Jin, and Ge Li. 2023 · 2023
Cited alongside, same era.
Constructing Effective In-Context Demonstration for Code Intelligence Tasks: An Empirical Study
Shuzheng Gao, Xin-Cheng Wen, Cuiyun Gao, Wenxuan Wang, and Michael R Lyu. 2023 · 2023
Cited alongside, same era.
Semantic Compression With Large Language Models
Henry Gilbert, Michael Sandborn, Douglas C Schmidt, Jesse Spencer-Smith, and Jules White. 2023 · 2023
Cited alongside, same era.
Large Language Models for Software Engineering: A Systematic Literature Review
Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang. 2023 · 2023
Cited alongside, same era.
SelfEvolve: A Code Evolution Framework via Large Language Models
Shuyang Jiang, Yuhao Wang, and Yu Wang. 2023 · 2023
Cited alongside, same era.
DS-1000: A natural and reliable benchmark for data science code generation. In International Conference on Machine Learning . PMLR, 18319–18345
Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Wen-tau Yih, Daniel Fried, Sida Wang, and Tao Yu. 2023 · 2023
Cited alongside, same era.
Enabling Programming Thinking in Large Language Models Toward Code Generation
Jia Li, Ge Li, Yongmin Li, and Zhi Jin. 2023a · 2023
Cited alongside, same era.
Cctest: Testing and repairing code completion systems. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 1238–1250
Zongjie Li, Chaozheng Wang, Zhibo Liu, Haoxuan Wang, Dong Chen, Shuai Wang, and Cuiyun Gao. 2023b · 2023
Cited alongside, same era.
A Multi-Step Learning Approach to Assist Code Review. In 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 450–460
Oussama Ben Sghaier and Houari Sahraoui. 2023 · 2023
Closest in time.
ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks
Giriprasad Sridhara, Sourav Mazumdar, et al · 2023
Closest in time.
Evaluating AIGC Detectors on Code Content
Jian Wang, Shangqing Liu, Xiaofei Xie, and Yi Li. 2023 · 2023
Closest in time.
How Effective Are Neural Networks for Fixing Security Vulnerabilities
Yi Wu, Nan Jiang, Hung Viet Pham, Thibaud Lutellier, Jordan Davis, Lin Tan, Petr Babkin, and Sameena Shah. 2023 · 2023
Closest in time.
Automated program repair in the era of large pre-trained language models. In Proceedings of the 45th International Conference on Software Engineering (ICSE 2023). Association for Computing Machinery
Chunqiu Steven Xia, Yuxiang Wei, and Lingming Zhang. 2023 · 2023
Closest in time.
Conversational automated program repair
Chunqiu Steven Xia and Lingming Zhang. 2023 · 2023
Closest in time.
18 million links in commit messages: purpose, evolution, and decay
Tao Xiao, Sebastian Baltes, Hideaki Hata, Christoph Treude, Raula Gaikovina Kula, Takashi Ishio, and Kenichi Matsumoto. 2023 · 2023
Closest in time.
Burak Yetiştiren, Işık Özsoy, Miray Ayerdem, and Eray Tüzün. 2023 · 2023
Closest in time.
Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions. In Proceedings of the ACM on Software Engineering (PACMSE)
Tao Xiao, Hideaki Hata, Christoph Treude, and Kenichi Matsumoto. 2024 · 2024
Closest in time.