Advertisement

Parallel search for LTL violations

  • Michael D. JonesEmail author
  • Jacob Sorber
Special section on parallel and distributed model checking

Abstract

Recent advances in parallel model checking for liveness properties achieve significant capacity increases over sequential model checkers. However, the capacity of parallel model checkers is in turn limited by available aggregate memory and network bandwidth. We propose a new parallel algorithm that sacrifices complete coverage for increased capacity to find errors. The algorithm, called BEE (for bee-based error exploration), uses coordinated depth-bounded random walks to reduce memory and bandwidth demands. A unique advantage of BEE is that it is well suited for use on clusters of nondedicated workstations.

Keywords

Semiformal verification Explicit model checking Biologically inspired algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barnat J, Brim L, Stříbrná J (2000) Distributed LTL model-checking in SPIN. Technical Report FIMU-RS-2000-10, Faculty of Informatics, Masaryk University, Brno, Czech RepublicGoogle Scholar
  2. 2.
    Bartholdi III JJ, Seeley TD, Tovey CA, Vande Vate JH (1993) The pattern and effectiveness of forager allocation among flower patches by honey bee colonies. J Theor Biol 160:23–40CrossRefGoogle Scholar
  3. 3.
    Brim L, Cerna I, Krcal P, Pelanek R (2001) Distributed LTL model checking based on negative cycle detection. In: Proceedings of the conference on foundations of software technology and theoretical computer scienceFST-TCS01, Bangalore, India, 13–15 December 2001. Lecture notes in computer science, vol 2245. Springer, Berlin Heidelberg New YorkGoogle Scholar
  4. 4.
    Brim L, Cerna I, Necesal M (2001) Randomization helps in LTL model checking. In: Proceedings of the PAPM-PROBMIV workshop, Aachen, Germany, 12–14 September 2001. Lecture notes in computer science, vol 2165, Springer, Berlin Heidelberg New YorkGoogle Scholar
  5. 5.
    Clarke EM, Grumberg O, Peled D (2000) Model checking. MIT Press, Cambridge, MAGoogle Scholar
  6. 6.
    Dill DL (1996) The Murφ verification system. In: Alur R, Henzinger TA (eds) Proceedings of Computer Aided Verification (CAV ’96), New Brunswick, NJ, July/August 1996. Lecture notes in computer science, vol 1102, Springer, Berlin Heidelberg New York, pp 390–393Google Scholar
  7. 7.
    Di Caro G, Dorigo M (1998) Antnet: distributed stigmergetic control for communications networks. J Artif Intell Res 9:317–365Google Scholar
  8. 8.
    Dorigo M, Di Caro G, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5(2):137–172CrossRefGoogle Scholar
  9. 9.
    Dang Z, Kemmerer R (2000) Three approximation techniques for ASTRAL symbolic model checking of infinite state real-time systems. In: Proceedings of the 22nd international conference on software engineering (ICSE00), Limerick Ireland, 4–11 June 2000. IEEE Press, New York, pp 345–354Google Scholar
  10. 10.
    Edelkamp S, Lluch-Lafuente A, Leue S (2001) Directed explicit model checking with HSF-SPIN. In: Proceedings of the 8th international SPIN workshop on model checking software, Toronto, Canada, 19–20 May 2001. Lecture notes in computer science, vol 2057, Springer, Berlin Heidelberg New YorkGoogle Scholar
  11. 11.
    Grumberg O, Heyman T, Schuster A (2001) Distributed symbolic model checking for the μ-calculus. In: Proceedings of Computer Aided Verification 2001 (CAV ’01), Paris, 18–23 July 2001. Lecture notes in computer science, vol 2102, Springer, Berlin Heidelberg New YorkGoogle Scholar
  12. 12.
    Haslum P (1999) Model checking by random walk. Available at: http://www.ida.liu.se/∼pahas/public/ccsse99.ps.gzGoogle Scholar
  13. 13.
    Holzmann GJ (1998) An analysis of bitstate hashing. Formal Meth Sys Design 13(3):289–307CrossRefGoogle Scholar
  14. 14.
    Lerda F, Sisto R (1999) Distributed-memory model checking in SPIN. In: Proceedings of the SPIN workshop, Trento, Italy, 5 July 1999. Lecture notes in computer science, vol 1680, Springer, Berlin Heidelberg New YorkGoogle Scholar
  15. 15.
    Russell SJ (1992) Efficient memory-bounded search methods. In: Proceedings of the 10th European conference on artificial intelligence (ECAI92). Wiley, New York, pp 1–5Google Scholar
  16. 16.
    Seeley TD (1995) The wisdom of the hive: the social biology of honey bee colonies. Harvard University Press, Cambridge, MAGoogle Scholar
  17. 17.
    Seeley TD, Visscher PK (1988) Assessing the benefits of cooperation in honeybee foraging: search costs, forage quality and competitive ability. Behav Ecol Sociobiol 22:229–237CrossRefGoogle Scholar
  18. 18.
    Seeley TD, Camazine S, Sneyd J (1991) Collective decision-making in honey bees: how colonies choose amung nectar sources. Behav Ecol Sociobiol 28:277–290CrossRefGoogle Scholar
  19. 19.
    Stern U, Dill DL (1997) Parallelizing the Murφ verifier. In: Grumburg O (ed) Proceedings of Computer Aided Verification (CAV’97), Haifa, Israel, June 1997. Lecture notes in computer science, vol 1254, Springer, Berlin Heidelberg New York, pp 256–267Google Scholar

Copyright information

© Springer-Verlag 2003

Authors and Affiliations

  1. 1.Department of Computer ScienceBrigham Young UniversityUtahUSA

Personalised recommendations