Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models-Reference-Cited by-同舟云学术

Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models

Published:2024-04-12 Issue: Volume:1 Page:1-13
ISSN:
Container-title:Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
language:
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Author:

Gao Shuzheng¹^ORCID,Mao Wenxin²^ORCID,Gao Cuiyun²^ORCID,Li Li³^ORCID,Hu Xing⁴^ORCID,Xia Xin⁴^ORCID,Lyu Michael R.¹^ORCID

Affiliation:

1. The Chinese University of Hong Kong, Hong Kong, China

2. Harbin Institute of Technology, Shenzhen, China

3. Beihang University, Beijing, China

4. Zhejiang University, Hangzhou, China

Funder

Research Grants Council of the Hong Kong Special Administrative Region, China

Natural Science Foundation of Guangdong Province

Shenzhen Basic Research

Shenzhen International Cooperation Project

Major Key Project of PCL

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3597503.3639216

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3. Antonio Valerio Miceli Barone and Rico Sennrich. 2017. A parallel corpus of Python functions and documentation strings for automated code documentation and code generation. arXiv preprint arXiv:1707.02275 (2017).

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