Scalability of CodeT5-Based Vulnerability Detection in IDEs for Incremental Code Changes

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: What is the scalability of CodeT5-based vulnerability detection in IDE environments when processing incremental code changes versus full-file analysis, as measured by latency per code edit and. In the rapidly evolving software development landscape, Python stands out for its simplicity, versatility, and extensive ecosystem. Python packages, as units of organization, reusability, and distribution, have become a pressing concern, highlighted by the considerable number of. 19 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the scalability of CodeT5-based vulnerability detection in IDE environments when processing incremental code changes versus full-file analysis, as measured by latency per code edit and detection accuracy on a modified CodeT5-Python dataset with injected vulnerabilities?

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