Deep random search for efficient model checking of timed automata

  • Authors:
  • R. Grosu;X. Huang;S. A. Smolka;W. Tan;S. Tripakis

  • Affiliations:
  • Dept. of CS, Stony Brook Univ., Stony Brook, NY;Dept. of CS, Stony Brook Univ., Stony Brook, NY;Dept. of CS, Stony Brook Univ., Stony Brook, NY;Dept. of CS, Stony Brook Univ., Stony Brook, NY;Verimag, Centre Equation, Gieres, France

  • Venue:
  • Proceedings of the 13th Monterey conference on Composition of embedded systems: scientific and industrial issues
  • Year:
  • 2006

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Abstract

We present DRS (Deep Random Search), a new Las Vegas algorithm for model checking safety properties of timed automata. DRS explores the state space of the simulation graph of a timed automaton by performing random walks up to a prescribed depth. Nodes along these walks are then used to construct a random fringe, which is the starting point of additional deep random walks. The DRS algorithm is complete, and optimal to within a specified depth increment. Experimental results show that it is able to find extremely deep counter-examples for a number of benchmarks, outperforming Open-Kronos and UPPAAL in the process.