Scaling Effects on Vulnerability Classification Accuracy in Llama3, Codestral, and DeepSeek R1

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: What is the impact of model size scaling on the pass@1 accuracy of Llama3, Codestral, and Deepseek R1 when evaluating vulnerability classification on the Big-Vul dataset. Recent advancements in generative AI have led to the widespread adoption of large language models (LLMs) in software engineering, addressing numerous long-standing challenges. However, a comprehensive study examining the capabilities of LLMs in software vulnerability detection. 14 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 2.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of model size scaling on the pass@1 accuracy of Llama3, Codestral, and Deepseek R1 when evaluating vulnerability classification 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