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January: A Parallel Algorithm for Bug Hunting Based on Insect Behavior

  • Peter Lamborn
  • Michael Jones
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)

Abstract

January is a group of interacting stateless model checkers designed for bug hunting in large transition graphs that represent the behavior of a program or protocol. January is based upon both individual and social insect behaviors, as such, dynamic solutions emerge from agents functioning with incomplete data. Each agent functions on a processor located on a network of workstations (NOW). The agents’ search pattern is a semi-random walk based on the behavior of the grey field slug (Agriolimax reticulatus), the house fly (Musca domestica), and the black ant (Lassius niger). January requires significantly less memory to detect bugs than the usual parallel approach to model checking. In some cases, January finds bugs using 1% of the memory needed by the usual algorithm to find a bug. January also requires less communication which saves time and bandwidth.

Keywords

Model Check Transition Graph Processing Node Negative Reinforcement Musca Domestica 
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 2006

Authors and Affiliations

  • Peter Lamborn
    • 1
  • Michael Jones
    • 2
  1. 1.Mississippi State University 
  2. 2.Brigham Young University 

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