Counterexample generation in CPS model checking based on ARSG algorithm
| Autor: | Weiwei Lu, Zining Cao, Fujun Wang, Mingguang Hu |
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| Rok vydání: | 2021 |
| Předmět: |
Model checking
Computer science business.industry media_common.quotation_subject Neighbourhood (graph theory) Cyber-physical system Computational Mathematics Software Debugging Computational Theory and Mathematics Hardware and Architecture TheoryofComputation_LOGICSANDMEANINGSOFPROGRAMS Modeling and Simulation Genetic algorithm Reinforcement learning business Algorithm media_common Counterexample |
| Zdroj: | International Journal of Computational Science and Engineering. 24:312 |
| ISSN: | 1742-7193 1742-7185 |
| DOI: | 10.1504/ijcse.2021.115658 |
| Popis: | With the rapid development of software and physical devices, cyber-physical systems (CPS) are widely adopted in many application areas. Due to the increasing complexities of these systems, it is difficult to detect defects in CPS models. Counterexample generation in CPS model checking is a good choice as it is able to find defects in CPS models efficiently and can provide meaningful diagnostic feedback to facilitate debugging. In many studies, robustness-guided counterexample generation of CPS is investigated by various optimisation methods, which falsify the given properties of a CPS. In this paper, we combine genetic algorithm (GA) with acceptance-rejection technique based on the neighbourhood of the input sequence space, and propose a novel algorithm which is called ARSG algorithm. The idea of this algorithm is similar to 'exploration-exploitation' in reinforcement learning. Finally, the new algorithm is compared with the cross-entropy algorithm and the genetic algorithm under different parameters, and the performance of the new algorithm is better than the other two algorithms. |
| Databáze: | OpenAIRE |
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