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

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 goal: 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?

Autonomous literature synthesis. Automated review score: 7.7/10. Full text and citation available at Assignee Research.