Open Science in Software Engineering: A Study on Deep Learning-Based Vulnerability Detection-Reference-Cited by-同舟云学术

Open Science in Software Engineering: A Study on Deep Learning-Based Vulnerability Detection

Published:2023-04-01 Issue:4 Volume:49 Page:1983-2005
ISSN:0098-5589
Container-title:IEEE Transactions on Software Engineering
language:
Short-container-title:IIEEE Trans. Software Eng.

Author:

Nong Yu¹,Sharma Rainy¹,Hamou-Lhadj Abdelwahab²^ORCID,Luo Xiapu³^ORCID,Cai Haipeng¹^ORCID

Affiliation:

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

2. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

3. Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China

Funder

Army Research Office

Office of Naval Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Software

Link

http://xplorestaging.ieee.org/ielx7/32/10103953/09894099.pdf?arnumber=9894099

Reference110 articles.

1. Dual-Component Deep Domain Adaptation: A New Approach for Cross Project Software Vulnerability Detection

2. End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks

3. Recurrent Neural Network Based Binary Code Vulnerability Detection

4. DeepBalance: Deep-Learning and Fuzzy Oversampling for Vulnerability Detection

5. Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks;zhou;Proc Adv Neural Inf Process Syst,2019

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