CodeT5-Based Vulnerability Detection Latency in IDE vs. Batch Processing

This report synthesises findings from 16 peer-reviewed papers addressing the following research question: How does integrating CodeT5-based vulnerability detection into IDE environments impact inference latency compared to standalone batch processing on the CodeT5-Python dataset. Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does integrating CodeT5-based vulnerability detection into IDE environments impact inference latency compared to standalone batch processing on the CodeT5-Python dataset?

Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.