Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Parallel search for LTL violations

  • Special section on parallel and distributed model checking
  • Published:
International Journal on Software Tools for Technology Transfer Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  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 Republic

  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 160:23–40

    Article  Google Scholar 

  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 York

  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 York

  5. Clarke EM, Grumberg O, Peled D (2000) Model checking. MIT Press, Cambridge, MA

  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 390–393

  7. Di Caro G, Dorigo M (1998) Antnet: distributed stigmergetic control for communications networks. J Artif Intell Res 9:317–365

    Google Scholar 

  8. Dorigo M, Di Caro G, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5(2):137–172

    Article  Google Scholar 

  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 345–354

  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 York

  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 York

  12. Haslum P (1999) Model checking by random walk. Available at: http://www.ida.liu.se/∼pahas/public/ccsse99.ps.gz

  13. Holzmann GJ (1998) An analysis of bitstate hashing. Formal Meth Sys Design 13(3):289–307

    Article  Google Scholar 

  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 York

  15. Russell SJ (1992) Efficient memory-bounded search methods. In: Proceedings of the 10th European conference on artificial intelligence (ECAI92). Wiley, New York, pp 1–5

  16. Seeley TD (1995) The wisdom of the hive: the social biology of honey bee colonies. Harvard University Press, Cambridge, MA

    Google Scholar 

  17. Seeley TD, Visscher PK (1988) Assessing the benefits of cooperation in honeybee foraging: search costs, forage quality and competitive ability. Behav Ecol Sociobiol 22:229–237

    Article  Google Scholar 

  18. Seeley TD, Camazine S, Sneyd J (1991) Collective decision-making in honey bees: how colonies choose amung nectar sources. Behav Ecol Sociobiol 28:277–290

    Article  Google Scholar 

  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 256–267

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael D. Jones.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jones, M., Sorber, J. Parallel search for LTL violations. Int J Softw Tools Technol Transfer 7, 31–42 (2005). https://doi.org/10.1007/s10009-003-0115-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10009-003-0115-8

Keywords