Neural Transfer Learning for Repairing Security Vulnerabilities in C Code-Reference-Cited by-同舟云学术

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Neural Transfer Learning for Repairing Security Vulnerabilities in C Code

Published:2023-01-01 Issue:1 Volume:49 Page:147-165
ISSN:0098-5589
Container-title:IEEE Transactions on Software Engineering
language:
Short-container-title:IIEEE Trans. Software Eng.

Author:

Chen Zimin¹^ORCID,Kommrusch Steve²^ORCID,Monperrus Martin¹^ORCID

Affiliation:

1. KTH Royal Institute of Technology, Stockholm, Sweden

2. Colorado State University, Fort Collins, CO, USA

Funder

Wallenberg Artificial Intelligence, Autonomous Systems and Software Program

Knut och Alice Wallenbergs Stiftelse

Swedish Foundation for Strategic Research

National Science Foundation

Vetenskapsrådet

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Software

Link

https://ieeexplore.ieee.org/ielam/32/10008953/9699412-aam.pdf

Reference81 articles.

1. Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning

2. Report on the Static Analysis Tool Exposition (SATE) IV

3. Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge

4. Infer: An Automatic Program Verifier for Memory Safety of C Programs

5. A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries

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1. Enhanced automated code vulnerability repair using large language models;Engineering Applications of Artificial Intelligence;2024-12

2. A cosine similarity-based labeling technique for vulnerability type detection using source codes;Computers & Security;2024-11

3. One Size Does Not Fit All: Multi-granularity Patch Generation for Better Automated Program Repair;Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis;2024-09-11

4. T-RAP: A Template-guided Retrieval-Augmented Vulnerability Patch Generation Approach;Proceedings of the 15th Asia-Pacific Symposium on Internetware;2024-07-24

5. A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback;Proceedings of the 1st ACM International Conference on AI-Powered Software;2024-07-10

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