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Prioritized Process Test: More Efficiency in Testing of Business Processes and Workflows

  • Miroslav BuresEmail author
  • Tomas CernyEmail author
  • Matej Klima
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 424)

Abstract

Testing business processes and workflows in information systems, while aiming to cover all possible paths, requires high efforts demanding considerable costs. In this paper, we propose an algorithm generating a path-based test cases from the system model, based on weighted directed graph. The approach brings an alternative to the currently established test requirements concept. The algorithm reflects various levels of priorities of particular functions in the tested system, previously defined by the test designer. When compared to simulated naive approaches based on reverse reduction of test set, our proposed algorithm produces more efficient test cases in terms of number of the total test steps, whilst keeping the same level of test coverage of the priority functions of the tested system.

Keywords

Decision Point Unique Action Priority Action Test Coverage System Under Test 
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.

References

  1. 1.
    Yoo, S., Harman, M.: Regression testing minimization, selection and prioritization: a survey. Softw. Test. Verification Reliab. 22(2), 67–120 (2010). WilleyCrossRefGoogle Scholar
  2. 2.
    Dwarakanath, A., Jankiti, A.: Minimum number of test paths for prime path and other structural coverage criteria. In: Merayo, M.G., Oca, E.M. (eds.) ICTSS 2014. LNCS, vol. 8763, pp. 63–79. Springer, Heidelberg (2014). doi: 10.1007/978-3-662-44857-1_5 Google Scholar
  3. 3.
    Nan, L., Fei, L., Offutt, J.: Better algorithms to minimize the cost of test paths. In: IEEE 5th International Conference on Software Testing, Verification and Validation, pp. 280–289. IEEE (2012)Google Scholar
  4. 4.
    Gökçe, N., Eminov, M., Belli, F.: Coverage-based, prioritized testing using neural network clustering. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds.) ISCIS 2006. LNCS, vol. 4263, pp. 1060–1071. Springer, Heidelberg (2006). doi: 10.1007/11902140_110 CrossRefGoogle Scholar
  5. 5.
    Belli, F., Eminov, M., Gökçe, N.: Coverage-oriented, prioritized testing – a fuzzy clustering approach and case study. In: Bondavalli, A., Brasileiro, F., Rajsbaum, S. (eds.) LADC 2007. LNCS, vol. 4746, pp. 95–110. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-75294-3_8 CrossRefGoogle Scholar
  6. 6.
    Panthi, V., Mohapatra, D.P.: Generating prioritized test sequences using firefly optimization technique. In: Jain, L.C., Behera, H.S., Mandal, J.K., Mohapatra, D.P. (eds.) SIST, vol. 32, pp. 627–635. Springer, Heidelberg (2015). doi: 10.1007/978-81-322-2208-8_57 Google Scholar
  7. 7.
    Achimugu, P., et al.: A systematic literature review of software requirements prioritization research. Inf. Softw. Technol. 56(6), 568–585 (2014)CrossRefGoogle Scholar
  8. 8.
    Koomen, T., Broekman, B., van der Aalst, L., Vroon, M.: TMap Next: for Result-Driven Testing. UTN Publishers, pp. 598–602 (2013)Google Scholar
  9. 9.
    Bures, M.: PCTgen: automated generation of test cases for application workflows. In: Rocha, A., Correia, A.M., Costanzo, S., Reis, L.P. (eds.) AISC, vol. 353, pp. 789–794. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-16486-1_78 CrossRefGoogle Scholar
  10. 10.
    van der Aalst, L., Roodenrijs, E., Vink, J., Baarda, R.: TMap Next: Business Driven Test Management, pp. 93–113. UTN Publishers (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Science, FEECzech Technical University in PraguePragueCzech Republic
  2. 2.Department of Computer ScienceBaylor UniversityWacoUSA

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