Semantic Similarity-Based Few-Shot Retrieval Reduces False Positives in Code Vulnerability Detection

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does semantic similarity-based few-shot example retrieval compare to random selection in reducing false positive rates for code vulnerability detection models on the Big-Vul benchmark. This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative. 2 claims were extracted from source literature; 2 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 Question

How does semantic similarity-based few-shot example retrieval compare to random selection in reducing false positive rates for code vulnerability detection models on the Big-Vul benchmark?

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