Search Results for author:
Found 5 papers, 1 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.
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.
Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation
Software testing is a crucial aspect of software development, and the creation of high-quality tests that adhere to best practices is essential for effective maintenance.
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.
