Code Property Graph Fidelity and GCN-Based False Positive Prediction Accuracy in SAST Tools

Abstract

Abstract: This report synthesises findings from 5 peer-reviewed papers addressing the following research question: What is the correlation between Code Property Graph representation fidelity and the classification accuracy of GCN-based false positive predictors across diverse SAST tools. Software vulnerabilities pose significant security challenges and potential risks to society, necessitating extensive efforts in automated vulnerability detection. There are two popular lines of work to address automated vulnerability detection. 5 claims were extracted from source literature; 5 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 Question

What is the correlation between Code Property Graph representation fidelity and the classification accuracy of GCN-based false positive predictors across diverse SAST tools?

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