New Study Shows Random Forest Models Can Spot 80% of Vulnerabilities Before Code Merge

The study evaluates a machine-learning framework for predicting vulnerable code changes, showing Random Forest delivers the highest accuracy, robust performance across reduced feature sets, and significantly stronger precision and recall during real-world online deployment using six years of AOSP data.