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Found 8 papers, 2 papers with code
A Comprehensive Study of the Capabilities of Large Language Models for Vulnerability Detection
Large Language Models (LLMs) have demonstrated great potential for code generation and other software engineering tasks.
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning
We demonstrated that using our cleaned data, LineVul, a SOTA line-level vulnerability detection tool, detected 70 more vulnerable lines and 18 more vulnerable functions, and improved Top 10 accuracy from 66% to 73%.
Do Language Models Learn Semantics of Code? A Case Study in Vulnerability Detection
Due to the different training objectives and the performance of the models, it is interesting to consider whether the models have learned the semantics of code relevant to vulnerability detection, namely bug semantics, and if so, how the alignment to bug semantics relates to model performance.
Towards Causal Deep Learning for Vulnerability Detection
Our results show that CausalVul consistently improved the model accuracy, robustness and OOD performance for all the state-of-the-art models and datasets we experimented.
MixQuant: Mixed Precision Quantization with a Bit-width Optimization Search
Quantization is a technique for creating efficient Deep Neural Networks (DNNs), which involves performing computations and storing tensors at lower bit-widths than f32 floating point precision.
An Empirical Study of Deep Learning Models for Vulnerability Detection
Deep learning (DL) models of code have recently reported great progress for vulnerability detection.
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection
In this paper, we propose to combine such causal-based vulnerability detection algorithms with deep learning, aiming to achieve more efficient and effective vulnerability detection.
DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs
Also, it can provide actionable insights for fix whereas DeepLocalize can only report faults that lead to numerical errors during training.
