Contrastive Learning Enhances Cross-Domain Code Embeddings for Vulnerability Classification

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: Can contrastive learning objectives improve the cross-domain transferability of code embedding models for vulnerability classification when tested on heterogeneous compiler optimization levels. 10 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: Can contrastive learning objectives improve the cross-domain transferability of code embedding models for vulnerability classification when tested on heterogeneous compiler optimization levels?

Autonomous literature synthesis. Automated review score: 8.3/10. Full text and citation available at Assignee Research.