A Transformer-based Model for Assisting Dockerfile Revising-Reference-Cited by-同舟云学术

A Transformer-based Model for Assisting Dockerfile Revising

Published:2024-04-14 Issue: Volume: Page:312-313
ISSN:
Container-title:Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings
language:
Short-container-title:

Author:

Wu Yiwen¹²^ORCID,Zhang Yang¹²^ORCID,Wang Tao¹²^ORCID,Wang Huaimin¹²^ORCID

Affiliation:

1. National University of Defense Technology, Changsha, China

2. State Key Laboratory of Complex & Critical Software Environment, Changsha, China

Funder

National Natural Science Foundation of China

Foundation of PDL

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3639478.3643083

Reference8 articles.

1. 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).

2. Jürgen Cito, Gerald Schermann, John Erik Wittern, Philipp Leitner, Sali Zumberi, and Harald C Gall. 2017. An empirical analysis of the docker container ecosystem on github. In MSR. IEEE, 323--333.

3. 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. In EMNLP. 1536--1547.

4. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019).

5. Changyuan Lin, Sarah Nadi, and Hamzeh Khazaei. 2020. A large-scale data set and an empirical study of docker images hosted on docker hub. In ICSME. IEEE, 371--381.