Abstract
We present an automated system repair framework for cyber-physical systems. The proposed framework consists of three main steps: (1) system simulation and fault detection to generate a labeled dataset, (2) identification of the repairable temporal properties leading to the faulty behavior and (3) repairing the system to avoid the occurrence of the cause identified in the second step. We express the cause as a past time signal temporal logic (ptSTL) formula and present an efficient monotonicity-based method to synthesize a ptSTL formula from a labeled dataset. Then, in the third step, we modify the faulty system by removing all behaviors that satisfy the ptSTL formula representing the cause of the fault. We apply the framework to two rich modeling formalisms: discrete-time dynamical systems and timed automata. For both of them, we define repairable formulae, the corresponding repair procedures, and illustrate them over case studies.
Highlights
From autonomous vehicles, to smart agriculture systems, medical devices and robotics, cyberphysical systems (CPSs) are in use in a very wide range of areas
As a part of the proposed framework, we present an efficient method to synthesize a past time signal temporal logic (ptSTL) formula from a given set of parametric formulae and a labeled dataset of system traces such that the evaluation of the resulting formula matches the labels
We presented an automated system repair framework for cyber-physical systems and showed its use on discrete-time dynamical systems and timed automata
Summary
To smart agriculture systems, medical devices and robotics, cyberphysical systems (CPSs) are in use in a very wide range of areas. The proposed framework consists of three mains steps: (1) generation of a labeled dataset via simulation and testing, (2) synthesis of a “repairable” past time signal temporal logic (ptSTL) formula that describes the labeled events and (3) performing the associated repair process for the identified formula. We require that the repair process does not introduce any new behavior We formalize these requirements over the system traces and parametric ptSTL formulae. We present a fully automated framework to find the causes of faulty behaviors and repair the system to avoid these causes for discrete-time dynamical systems and timed automata. Considering that the faulty behavior can have multiple causes, our synthesis method iteratively generates a formula as a disjunction of optimized formulae from the given set. A candidate set of parametric formulae are optimized, and the best formula is added to the final formula via disjunction until it is not possible to further improve the final formula
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