Interpretability-Driven Feature Engineering in Binary Code Similarity for Vulnerability Classification

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: To what extent does the interpretability of feature engineering in binary code similarity analysis influence the accuracy of downstream vulnerability classification tasks when compared to black-box. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: To what extent does the interpretability of feature engineering in binary code similarity analysis influence the accuracy of downstream vulnerability classification tasks when compared to black-box model approaches, as measured by F1-score on the Big-Vul dataset?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.