Real-Time Vulnerability Classification Trade-offs in CodeT5 and State-of-the-Art Code Models

Abstract

Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: How does the trade-off between real-time vulnerability classification accuracy and throughput compare between CodeT5 models and other state-of-the-art code language models (e.g., CodeGen, CodeGPT). Many ML-based approaches have been proposed to automatically detect, localize, and repair software vulnerabilities. While ML-based methods are more effective than program analysis-based vulnerability analysis tools, few have been integrated into modern IDEs, hindering practical. 15 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 2.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the trade-off between real-time vulnerability classification accuracy and throughput compare between CodeT5 models and other state-of-the-art code language models (e.g., CodeGen, CodeGPT) when deployed as Visual Studio Code extensions?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance