Time Constraint Removal and Model Accuracy on Big-Vul: A Multi-Study Synthesis

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: To what extent does removing time constraints improve the accuracy of DeepSeek R1 on the Big-Vul dataset compared to Codestral, and is this performance gain consistent across different vulnerability. Since the last comprehensive review in 1974, the Health Belief Model (HBM) has continued to be the focus of considerable theoretical and research attention. This article presents a critical review of 29 HBM-related investigations published during the period of 1974-1984. 15 claims were extracted from source literature; 12 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

To what extent does removing time constraints improve the accuracy of DeepSeek R1 on the Big-Vul dataset compared to Codestral, and is this performance gain consistent across different vulnerability classes?

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