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Found 7 papers, 2 papers with code
VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements
Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts.
Data-Driven AI Model Signal-Awareness Enhancement and Introspection
AI modeling for source code understanding tasks has been making significant progress, and is being adopted in production development pipelines.
D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis
However, existing datasets to train models for vulnerability identification suffer from multiple limitations such as limited bug context, limited size, and synthetic and unrealistic source code.
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization
We measure the signal awareness of models using a new metric we propose- Signal-aware Recall (SAR).
Exploring Software Naturalness through Neural Language Models
We explore this hypothesis through the use of a pre-trained transformer-based language model to perform code analysis tasks.
Learning to map source code to software vulnerability using code-as-a-graph
We explore the applicability of Graph Neural Networks in learning the nuances of source code from a security perspective.
GaDei: On Scale-up Training As A Service For Deep Learning
By evaluating the NLC workloads, we show that only the conservative hyper-parameter setup (e. g., small mini-batch size and small learning rate) can guarantee acceptable model accuracy for a wide range of customers.
