DeepSeek-R1 Context Window Scaling for Security Vulnerability Detection in Code

Abstract

Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: What is the impact of context window scaling on the security vulnerability detection performance of DeepSeek-R1 compared to other models across different code lengths and complexity levels. Many studies have developed Machine Learning (ML) approaches to detect Software Vulnerabilities (SVs) in functions and fine-grained code statements that cause such SVs. However, there is little work on leveraging such detection outputs for data-driven SV assessment to give. 16 claims were extracted from source literature; 2 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of context window scaling on the security vulnerability detection performance of DeepSeek-R1 compared to other models across different code lengths and complexity levels?

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