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Ant Colony Optimization for Model Checking

  • Enrique Alba
  • Francisco Chicano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4739)

Abstract

Model Checking is a well-known and fully automatic technique for checking software properties, usually given as temporal logic formulae on the program variables. Most model checkers found in the literature use exact deterministic algorithms to check the properties. These algorithms usually require huge amounts of computational resources if the checked model is large. We propose here the use of Ant Colony Optimization (ACO) to refute safety properties in concurrent systems. ACO algorithms are stochastic techniques belonging to the class of metaheuristic algorithms and inspired by the foraging behaviour of real ants. The results state that ACO algorithms find optimal or near optimal error trails in faulty concurrent systems with a reduced amount of resources, outperforming algorithms that are the state-of-the-art in model checking. This fact makes them suitable for checking safety properties in large concurrent systems, in which traditional techniques fail to find errors because of the model size.

Keywords

Model Check Linear Temporal Logic Safety Property Depth First Search Concurrent System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Enrique Alba
    • 1
  • Francisco Chicano
    • 1
  1. 1.GISUM Group, Departamento de Lenguajes y Ciencias de la Computación, University of MálagaSpain

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