Real-Time Vulnerability Classification Trade-offs in CodeT5: Client-Side vs. Server-Side Deployment

This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the trade-off between real-time vulnerability classification accuracy and throughput for CodeT5 models when deployed as Visual Studio Code extensions versus server-side APIs. Transformer-based language models for automatic code completion have shown great promise so far, yet the evaluation of these models rarely uses real data. This study provides both quantitative and qualitative assessments of three public code language models when completing. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the trade-off between real-time vulnerability classification accuracy and throughput for CodeT5 models when deployed as Visual Studio Code extensions versus server-side APIs?

Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.