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Found 5 papers, 1 papers with code
Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG
In addition, our user study shows that the vulnerability knowledge generated by Vul-RAG can serve as high-quality explanations which can improve the manual detection accuracy from 0. 60 to 0. 77.
A general approach to enhance the survivability of backdoor attacks by decision path coupling
One of the mainstream backdoor defenses is model reconstruction-based.
Understanding the Complexity and Its Impact on Testing in ML-Enabled Systems
Machine learning (ML) enabled systems are emerging with recent breakthroughs in ML.
Understanding Performance Problems in Deep Learning Systems
To bridge this gap, we present the first comprehensive study to i) characterize symptoms, root causes, and introducing and exposing stages of PPs in DL systems developed in TensorFLow and Keras, with 224 PPs collected from 210 StackOverflow posts, and to ii) assess the capability of existing performance analysis approaches in tackling PPs, with a constructed benchmark of 58 PPs in DL systems.
LEOPARD: Identifying Vulnerable Code for Vulnerability Assessment through Program Metrics
The latter needs the prior knowledge of known vulnerabilities and can only identify similar but not new types of vulnerabilities.
Software Engineering
