@harlowent

Automated tools for smart contract vulnerability detection often suffer from high false positives (FPs), reducing developer trust. This study optimizes FP rates by combining static analysis with symbolic execution, refining pattern matching to context-aware rules. Machine learning models trained on labeled datasets distinguish true vulnerabilities from benign patterns. Hybrid approaches integrate runtime verification for dynamic FP reduction. The optimized tool achieves >90% precision, enabling efficient, reliable audits for complex DeFi protocols.

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