Advertisement

Modeling Input Space for Testing Scientific Computational Software: A Case Study

  • Sergiy A. Vilkomir
  • W. Thomas Swain
  • Jesse H. Poore
  • Kevin T. Clarno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5103)

Abstract

An application of a method of test case generation for scientific computational software is presented. NEWTRNX, neutron transport software being developed at Oak Ridge National Laboratory, is treated as a case study. A model of dependencies between input parameters of NEWTRNX is created. Results of NEWTRNX model analysis and test case generation are evaluated.

References

  1. 1.
    Clarno, K., de Almeida, V., d’Azevedo, E., de Oliveira, C., Hamilton, S.: GNES-R: Global Nuclear Energy Simulator for Reactors Task 1: High-Fidelity Neutron Transport. In: Proceedings of PHYSOR–2006, American Nuclear Society Topical Meeting on Reactor Physics: Advances in Nuclear Analysis and Simulation, Vancouver, Canada (2006) Google Scholar
  2. 2.
    Cohen, D.M., Dalal, S.R., Fredman, M.L., Patton, G.C.: The AETG system: An approach to testing based on combinatorial design. IEEE Transactions on Software Engineering 23(7), 437–444 (1997)CrossRefGoogle Scholar
  3. 3.
    Cox, M.G., Harris, P.M.: Design and use of reference data sets for testing scientific software. Analytica Chimica Acta 380(2), 339–351 (1999)CrossRefGoogle Scholar
  4. 4.
    Einarsson, B. (ed.): Accuracy and Reliability in Scientific Computing. SIAM, Philadelphia (2005)zbMATHGoogle Scholar
  5. 5.
    Grindal, M., Offutt, J., Andler, S.F.: Combination testing strategies: A survey. Software Testing, Verification, and Reliability 15(3), 167–199 (2005)CrossRefGoogle Scholar
  6. 6.
    Hatton, L.: The T experiments: errors in scientific software. IEEE Computational Science and Engineering 4(2), 27–38 (1997)CrossRefGoogle Scholar
  7. 7.
    Howden, W.: Validation of scientific programs. Comput. Surv. 14(2), 193–227 (1982)CrossRefGoogle Scholar
  8. 8.
    Lewis, E., Miller Jr., W.F.: Computational Methods of Neutron Transport. ANS (1993) Google Scholar
  9. 9.
    Prowell, S.: TML: A description language for Markov chain usage models. Information and Software Technology 42(12), 835–844 (2000)CrossRefGoogle Scholar
  10. 10.
    Prowell, S.: JUMBL: A Tool for Model-Based Statistical Testing. In: Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS 2003), Big Island, HI, USA (2003)Google Scholar
  11. 11.
    Swain, W.T., Scott, S.L.: Model-Based Statistical Testing of a Cluster Utility. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3514, pp. 443–450. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Vilkomir, S.A., Swain, W.T, Poore, J.H.: Combinatorial test case selection with Markovian usage models. In: Proceedings of the 5th International Conference on Information Technology: New Generations (ITNG 2008), Las Vegas, Nevada, USA (2008) Google Scholar
  13. 13.
    Walton, G., Poore, J.H., Trammell, C.: Statistical Testing of Software Based on a Usage Model. Software: Practice and Experience 25(1), 97–108 (1995)CrossRefGoogle Scholar
  14. 14.
    Whittaker, J., Poore, J.H.: Markov Analysis of Software Specifications. ACM Transactions on Software Engineering and Methodology 2(1), 93–106 (1993)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sergiy A. Vilkomir
    • 1
  • W. Thomas Swain
    • 1
  • Jesse H. Poore
    • 1
  • Kevin T. Clarno
    • 2
  1. 1.Software Quality Research Laboratory, Department of Electrical Engineering and Computer ScienceUniversity of TennesseeKnoxvilleUSA
  2. 2.Reactor Analysis Group, Nuclear Science and Technology DivisionOak Ridge National LaboratoryOak RidgeUSA

Personalised recommendations