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

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Machine-learning framework using Random Forest achieves ~80% vulnerability recall and 98% precision in real-world code review and deployment scenarios.
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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.