Skip to main content

Towards the Automatic Identification of Faulty Multi-Agent Based Simulation Runs Using MASTER

  • Conference paper
Multi-Agent-Based Simulation XIII (MABS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7838))

Abstract

Testing a multi-agent based model is a tedious process that involves generating very many simulation runs, for example as a result of a parameter sweep. In practice, each simulation run must be inspected manually to gain complete confidence that the agent-based model has been implemented correctly and is operating according to expectations. We present MASTER, a tool which aims to semi-automatically detect when a simulation run has deviated from “normal” behaviour. A simulation run is flagged as “suspicious” when certain parameters traverse normal bounds determined by the modeller. These bounds are defined in reference to a small series of actual executions of the model deemed to be correct. The operation of MASTER is presented with two case studies, the first with the well-known “flockers” model supplied with the popular MASON agent-based modelling toolkit, and the second a skin tissue model written using another toolkit—FLAME.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 72.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. JUnit, http://www.junit.org (accessed: April 2012)

  2. libAnomaly, http://www.cs.ucsb.edu/~seclab/projects/libanomaly/index.html (accessed: April 2012)

  3. Bentley, K., Gerhardt, H., Bates, P.: Agent-based simulation of notch-mediated tip cell selection in angiogenic sprout initialisation. Journal of Theoretical Biology 250, 25–36 (2008)

    Article  Google Scholar 

  4. Buchanan, M.: Meltdown modelling. Could agent-based computer models prevent another financial crisis? Nature 460(7256), 680–682 (2009)

    Article  Google Scholar 

  5. Chang, G., Roth, C.B., Reyes, C.L., Pornillos, O., Chen, Y.-J., Chen, A.P.: Science 314, 1875 (2006); Retraction of: Pornillos, et al.: Science 310(5756), 1950-1953; Reyes, Chang: Science 308(5724), 1028-1031; Chang, Roth: Science 293(5536), 1793–1800

    Google Scholar 

  6. Coelho, R., Cirilo, E., Kulesza, U., von Staa, A., Rashid, A., Lucena, C.: JAT: A Test Automation Framework for Multi-Agent Systems. In: 2007 IEEE International Conference on Software Maintenance, pp. 425–434 (October 2007)

    Google Scholar 

  7. Cova, M., Kruegel, C., Vigna, G.: Detection and analysis of drive-by-download attacks and malicious javascript code. In: Proceedings of the International World Wide Web Conference (WWW 2010), pp. 281–290. ACM Press (2010)

    Google Scholar 

  8. Farmer, J., Foley, D.: The economy needs agent-based modelling. Nature 460(7256), 685–686 (2009)

    Article  Google Scholar 

  9. Jia, Y., Harman, M.: An analysis and survey of the development of mutation testing. IEEE Transactions on Software Engineering 37(5), 649–678 (2011)

    Article  Google Scholar 

  10. Kiran, M., Richmond, P., Holcombe, M., Chin, L.S., Worth, D., Greenough, C.: FLAME: simulating large populations of agents on parallel hardware architectures. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), pp. 1633–1636. ACM Press (2010)

    Google Scholar 

  11. Kruegel, C., Mutz, D., Valeur, F., Vigna, G.: On the detection of anomalous system call arguments. In: Snekkenes, E., Gollmann, D. (eds.) ESORICS 2003. LNCS, vol. 2808, pp. 326–343. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  12. Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K.: Mason: A new multi-agent simulation toolkit. In: Proceedings of the 2004 SwarmFest Workshop (2004)

    Google Scholar 

  13. Ma, Y.-S., Offutt, J., Kwon, Y.-R.: MuJava: An automated class mutation system. Journal of Software Testing, Verification and Reliability, 97–133 (2005)

    Google Scholar 

  14. Muaz Niazi, A.H., Kolberg, M.: Verification and validation of agent-based simulation using the vomas approach. In: Proceedings of the Third Workshop on Multi-Agent Systems and Simulation 2009, MASS 2009 (2009)

    Google Scholar 

  15. Nguyen, C., Perini, A.: Automated continuous testing of multi-agent systems. In: Workshop on Multi-Agent Systems (2007)

    Google Scholar 

  16. Simons, K.: Model error—evaluation of various finance models. New England Economic Review, 17–28 (1997)

    Google Scholar 

  17. Sun, T., McMinn, P., Coakley, S., Holcombe, M., Smallwood, R., MacNeil, S.: An integrated systems biology approach to understanding the rules of keratinocyte colony formation. Journal of the Royal Society Interface 4, 1077–1092 (2007)

    Article  Google Scholar 

  18. Zhang, Z., Thangarajah, J., Padgham, L.: Automated unit testing for agent systems. In: 2nd International Working Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2007), pp. 10–18. Citeseer (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wright, C.J., McMinn, P., Gallardo, J. (2013). Towards the Automatic Identification of Faulty Multi-Agent Based Simulation Runs Using MASTER. In: Giardini, F., Amblard, F. (eds) Multi-Agent-Based Simulation XIII. MABS 2012. Lecture Notes in Computer Science(), vol 7838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38859-0_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38859-0_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38858-3

  • Online ISBN: 978-3-642-38859-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics