Code-centric learning-based just-in-time vulnerability detection

Nguyen, Son; Nguyen, Thu-Trang; Vu, Thanh Trong; Do, Thanh-Dat; Ngo, Kien-Tuan; Vo, Hieu Dinh

doi:10.1016/j.jss.2024.112014

Journal of Systems and Software

2024

DOI: 10.1016/j.jss.2024.112014

|View full text |Cite

Code-centric learning-based just-in-time vulnerability detection

Son Nguyen,

Thu-Trang Nguyen,

Thanh Trong Vu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 8 publications

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A comprehensive analysis on software vulnerability detection datasets: trends, challenges, and road ahead

Guo,

Bettaieb,

Casino

2024

Int. J. Inf. Secur.

View full text Add to dashboard Cite

As society’s dependence on information and communication systems (ICTs) grows, so does the necessity of guaranteeing the proper functioning and use of such systems. In this context, it is critical to enhance the security and robustness of the DevSecOps pipeline through timely vulnerability detection. Usually, AI-based models enable desirable features such as automation, performance, and efficacy. However, the quality of such models highly depends on the datasets used during the training stage. The latter encompasses a series of challenges yet to be solved, such as access to extensive labelled datasets with specific properties, such as well-represented and balanced samples. This article explores the current state of practice of software vulnerability datasets and provides a classification of the main challenges and issues. After an extensive analysis, it describes a set of guidelines and desirable features that datasets should guarantee. The latter is applied to create a new dataset, which fulfils these properties, along with a descriptive comparison with the state of the art. Finally, a discussion on how to foster good practices among researchers and practitioners sets the ground for further research and continued improvement within this critical domain.

show abstract

A comprehensive analysis on software vulnerability detection datasets: trends, challenges, and road ahead

Guo,

Bettaieb,

Casino

2024

Int. J. Inf. Secur.

View full text Add to dashboard Cite

show abstract

A cosine similarity-based labeling technique for vulnerability type detection using source codes

Öztürk

2024

Computers & Security

View full text Add to dashboard Cite

Automated description generation for software patches

Vu,

Bui,

et al. 2025

Information and Software Technology

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Code-centric learning-based just-in-time vulnerability detection

Cited by 8 publications

References 37 publications

A comprehensive analysis on software vulnerability detection datasets: trends, challenges, and road ahead

A comprehensive analysis on software vulnerability detection datasets: trends, challenges, and road ahead

A cosine similarity-based labeling technique for vulnerability type detection using source codes

Automated description generation for software patches

Contact Info

Product

Resources

About