Advertisement

A Template for Categorizing Business Processes in Empirical Research

Chapter
  • 256 Downloads

Abstract

Empirical research is becoming increasingly important for understanding the practical uses of and problems with business processes technology in the field. However, no standardization on how to report observations and findings exists. This sometimes leads to research outcomes which report partial or incomplete data and makes published results of replicated studies on different data sets hard to compare. In order to help the research community improve reporting on business process models and collections and their characteristics, this chapter defines a modular template with the aim of reports’ standardization, which could also facilitate the creation of shared business process repositories to foster further empirical research in the future. The template has been positively evaluated by representatives from both BPM research and industry. The survey feedback has been incorporated in the template. We have applied the template to describe a real-world executable WS-BPEL process collection, measured from a static and dynamic perspective.

Keywords

Empirical research Metadata template Business process Business process description Business process metrics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

The authors would like to thank all of the participants in the survey for their time and valuable feedback.

References

  1. 1.
    E. Alemneh et al., A static analysis tool for BPEL source codes. Int. J. Comput. Sci. Mob. Comput. 3(2), 659–665 (2014)Google Scholar
  2. 2.
    W. Berli, D. Lübke, W. Möckli, Terravis – large scale business process integration between public and private partners, in Lecture Notes in Informatics (LNI), vol. P-232, ed. by E. Plödereder, L. Grunske, E. Schneider, D. Ull (Gesellschaft für Informatik e.V., Bonn, 2014), pp. 1075–1090Google Scholar
  3. 3.
    J. Cardoso, Complexity analysis of BPEL web processes. Softw. Process Improv. Pract. J., 12, 35–49 (2006)CrossRefGoogle Scholar
  4. 4.
    J. Cardoso, Business process control-flow complexity: metric, evaluation, and validation. Int. J. Web Serv. Res. 5(2), 49–76 (2008)CrossRefGoogle Scholar
  5. 5.
    J. Cardoso, J. Mendling, G. Neumann, H.A Reijers, A discourse on complexity of process models, in International Conference on Business Process Management (Springer, Berlin, 2006), pp. 117–128CrossRefGoogle Scholar
  6. 6.
    R.-H. Eid-Sabbagh, M. Kunze, A. Meyer, M. Weske, A platform for research on process model collections, in International Workshop on Business Process Modeling Notation (Springer, Berlin, 2012), pp. 8–22Google Scholar
  7. 7.
    Executive Office of the President – Office of Management and Budget, North American Industry Classification System (2017)Google Scholar
  8. 8.
    M. Hertis, M.B. Juric, An empirical analysis of business process execution language usage. IEEE Trans. Softw. Eng. 40(08), 738–757 (2014)CrossRefGoogle Scholar
  9. 9.
    C. Houy, P. Fettke, P. Loos, Empirical research in business process management-analysis of an emerging field of research. Bus. Process Manag. J. 16(4), 619–661 (2010)CrossRefGoogle Scholar
  10. 10.
    D. Jordan et al., Web Services Business Process Execution Language Version 2.0. OASIS (2007)Google Scholar
  11. 11.
    D. Lübke, Using metric time lines for identifying architecture shortcomings in process execution architectures, in 2015 IEEE/ACM 2nd International Workshop on Software Architecture and Metrics (SAM) (IEEE, Florence, 2015), pp. 55–58Google Scholar
  12. 12.
    D. Lübke, A. Ivanchikj, C. Pautasso, A template for categorizing business processes in empirical research, in Proceedings of the Business Process Management Forum (BPM 2017), vol. 297, ed. by J. Carmona, G. Engels, A. Kumar. LNBIP (Springer, Cham, 2017), pp. 36–52Google Scholar
  13. 13.
    C. Mao, Control and data complexity metrics for web service compositions, in Proceedings of the 10th International Conference on Quality Software (2010)Google Scholar
  14. 14.
    J. Mendling, Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness, 1st edn. (Springer, Berlin, 2008)CrossRefGoogle Scholar
  15. 15.
    J. Mendling, Empirical studies in process model verification, in Transactions on Petri Nets and Other Models of Concurrency II (Springer, Berlin, 2009), pp. 208–224Google Scholar
  16. 16.
    M. Skouradaki, D. Roller, C. Pautasso, F. Leymann, “bpelanon”: anonymizing bpel processes, in ZEUS (2014), pp. 1–7. CiteseerGoogle Scholar
  17. 17.
    J. Vanhatalo, J. Koehler, F. Leymann, Repository for business processes and arbitrary associated metadata, in Proceedings of the Demo Session of the 4th International Conference on Business Process Management (2006)Google Scholar
  18. 18.
    B. Weber, B. Mutschler, M. Reichert, Investigating the effort of using business process management technology: results from a controlled experiment. Sci. Comput. Program. 75(5), 292–310 (2010)MathSciNetCrossRefGoogle Scholar
  19. 19.
    B. Wetzstein, S. Strauch, F. Leymann, Measuring performance metrics of WS-BPEL service compositions, in Proceedings of ICNS (2009), pp. 49–56Google Scholar
  20. 20.
    C. Wohlin, P. Runeson, M. Höst, M.C. Ohlsson, B. Regnell, A. Wesslén, Experimentation in Software Engineering (Springer, Berlin, 2012)CrossRefGoogle Scholar
  21. 21.
    Z. Yan, R. Dijkman, P. Grefen, Business process model repositories–framework and survey. Inf. Softw. Technol. 54(4), 380–395 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.FG Software EngineeringLeibniz Universität HannoverHannoverGermany
  2. 2.Software Institute, Faculty of InformaticsUSILuganoSwitzerland

Personalised recommendations