Stratified and Random Sampling Effects on F1 Variance and Efficiency in Code Vulnerability Detection

Abstract

Abstract: This report synthesises findings from 7 peer-reviewed papers addressing the following research question: How does the choice of stratified versus random sampling affect the trade-off between F1-score variance and computational efficiency in Llama3 and Codestral when detecting code vulnerabilities with. Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the choice of stratified versus random sampling affect the trade-off between F1-score variance and computational efficiency in Llama3 and Codestral when detecting code vulnerabilities with contaminated training data?

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