Do Pretrained Language Models Indeed Understand Software Engineering Tasks?-Reference-Cited by-同舟云学术

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Do Pretrained Language Models Indeed Understand Software Engineering Tasks?

Published:2023-10-01 Issue:10 Volume:49 Page:4639-4655
ISSN:0098-5589
Container-title:IEEE Transactions on Software Engineering
language:
Short-container-title:IIEEE Trans. Software Eng.

Author:

Li Yao¹^ORCID,Zhang Tao¹^ORCID,Luo Xiapu²^ORCID,Cai Haipeng³^ORCID,Fang Sen¹^ORCID,Yuan Dawei¹^ORCID

Affiliation:

1. School of Computer Science and Engineering, Macau University of Science and Technology, Macao, China

2. Department of Computing, Hong Kong Polytechnic University, Hong Kong, China

3. School of Electrical Engineering and Computer Science, Washington State University Pullman, WA, USA

Funder

Macao Science and Technology Development Fund

Hong Kong RGC

HKPolyU

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Software

Link

http://xplorestaging.ieee.org/ielx7/32/10286436/10232920.pdf?arnumber=10232920

Reference79 articles.

1. Deep code search

2. Recursive deep models for semantic compositionality over a sentiment treebank;socher;Proc EMNLP,2013

3. "Why Should I Trust You?"

4. Corpus of linguistic acceptability;warstadt,2018

5. A Transformer-based Approach for Source Code Summarization

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing Bug Report Summaries Through Knowledge-Specific and Contrastive Learning Pre-Training;IEEE Access;2024

2. SumLLaMA: Efficient Contrastive Representations and Fine-Tuned Adapters for Bug Report Summarization;IEEE Access;2024

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