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Cyber-Physical Systems (CPS) have been widely deployed in safety-criticaldomains such as transportation, power and energy. Recently, there comes anincreasing demand in employing deep neural networks (DNNs) in CPS for moreintelligent control and decision making in sophisticated industrialsafety-critical conditions, giving birth to the class of DNN controllers.However, due to the inherent uncertainty and opaqueness of DNNs, concerns aboutthe safety of DNN-enabled CPS are also surging. In this work, we propose anautomated framework named AutoRepair that, given a safety requirement,identifies unsafe control behavior in a DNN controller and repairs them throughan optimization-based method. Having an unsafe signal of system execution,AutoRepair iteratively explores the control decision space and searches for theoptimal corrections for the DNN controller in order to satisfy the safetyrequirements. We conduct a comprehensive evaluation of AutoRepair on 6instances of industry-level DNN-enabled CPS from different safety-criticaldomains. Evaluation results show that AutoRepair successfully repairs criticalsafety issues in the DNN controllers, and significantly improves thereliability of CPS.

AutoRepair: Automated Repair for AI-Enabled Cyber-Physical Systems under Safety-Critical Conditions