Convolutional and Graph Neural Networks for Code Property Graph Analysis with Commit Embeddings

Abstract

Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: What is the effect of using convolutional neural networks versus graph neural networks on the inference efficiency and detection accuracy when processing code property graphs augmented with commit. The increasing complexity of modern software systems has led to a rise in vulnerabilities that malicious actors can exploit. Traditional methods of vulnerability detection, such as static and dynamic analysis, have limitations in scalability and automation. 12 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the effect of using convolutional neural networks versus graph neural networks on the inference efficiency and detection accuracy when processing code property graphs augmented with commit message embeddings?

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