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LLMs excel at generating functional RTL code but fail to meet security standards, with a mere 14-35% passing rate in security tests.
EAVA not only predicts software vulnerabilities but also provides actionable evidence, bridging the gap between automated assessments and human validation.
Execution signals and reasoning signals have orthogonal errors, and their independent calibration can significantly enhance Verilog code generation accuracy.
A single click with SmellCC can eliminate 96.8% of Python code smells, transforming how developers manage technical debt.
AgentExecutor outperforms existing methods by achieving up to 94% code coverage while slashing execution time by over 80%.
