On the effectiveness of graph data augmentation for source code learning-Reference-Cited by-同舟云学术

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On the effectiveness of graph data augmentation for source code learning

Published:2024-02 Issue: Volume:285 Page:111328
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Dong Zeming^ORCID,Hu Qiang^ORCID,Zhang Zhenya^ORCID,Zhao Jianjun^ORCID

Funder

Japan Society for the Promotion of Science

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference74 articles.

1. A survey of machine learning for big code and naturalness;Allamanis;ACM Comput. Surv.,2018

2. A systematic review on code clone detection;Ain;IEEE Access,2019

3. Vuldeepecker: A deep learning-based system for vulnerability detection;Li,2018

4. CodeNet: A large-scale AI for code dataset for learning a diversity of coding tasks;Puri,2021

5. M. Allamanis, M. Brockschmidt, M. Khademi, Learning to represent programs with graphs, in: International Conference on Learning Representations, ICLR, 2018.

Cited by 1 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. On the effectiveness of hybrid pooling in mixup-based graph learning for language processing;Journal of Systems and Software;2024-10

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