DeepSeek R1 and Claude Efficiency-Accuracy Trade-offs in Secure Code Review Pipelines

Abstract

Abstract: This report synthesises findings from 1 peer-reviewed paper addressing the following research question: What is the efficiency-accuracy trade-off when deploying Deepseek R1 and Claude in secure code review pipelines, measured by inference latency and vulnerability detection F1-scores on the Big-Vul. Large language models (LLMs) have demonstrated strong capability for code understanding and vulnerability detection. However, most existing approaches rely on static prompting and treat the model as a passive predictor, limiting adaptability under uncertainty, particularly in. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the efficiency-accuracy trade-off when deploying Deepseek R1 and Claude in secure code review pipelines, measured by inference latency and vulnerability detection F1-scores on the Big-Vul dataset?

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