ML-generated patches use binary code and vulnerability prompts to speed embedded software fixes while preserving security and functionality.
Machine learning generates and evaluates embedded software patches from binary code, cutting manual patch time while preserving fix reliability.
Decompiled controller binaries are converted into AST-based Modelica models that stay interpretable to SMEs and scale to complex code.