Measures of Quality in Business Process Modeling

  • Josef PavlicekEmail author
  • Petra Pavlickova
  • Pavel Naplava
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 366)


Business process modeling is undoubtedly one of the most important parts of Applied (Business) Informatics. Quality of business process models (diagrams) is crucial for any purpose in this area. The goal of a process analyst’s work is to create generally understandable, explicit, unambiguous and error-free models. If a process is properly described, created models can be used as an input into deep analysis and optimization. Optimization is mostly focused on a higher efficiency of the process or at least on a better clarification of its meaning and working. Objective: It can be assumed that properly designed business process models (similarly as in the case of correctly written algorithms) contain characteristics that can be mathematically described. If it is possible to find measurable attributes of business process model’s quality, it will be possible to define a different quality maturity levels of business process modeling results. Furthermore, it will be possible creating a tool helping process analysts designing proper models. Method: A systematic literature review was conducted in order to find and analyze business process model’s design and business process model’s quality measuring methods. Results: It was found that mentioned area had already been subject of research investigation in the past. Thirty-three suitable scientific publications and twenty-two quality measures were found. Conclusions: Analyzed articles and existing quality measures do not reflect all important attributes of business process model’s clarity, simplicity and completeness. Therefore it would be appropriate adding new measures of quality.


Business process modeling Business processes Measures of quality BPMN 


  1. 1.
    Hronza, R., Špeta, M.: Business Process Center of Excellence at the Faculty of Electrical Engineering at the Czech Technical University in Prague. In: 2013 IEEE 15th Conference on Business Informatics, pp. 346–349 (2013)Google Scholar
  2. 2.
    Pavel, N., Radek, H., Jan, K., Josef, P.: How to successfully start the transformation of an academic institution. Case study on the process mapping project at the Czech Technical University. In: Complementary proceedings of the 8th Workshop on Transformation & Engineering of Enterprises (TEE 2014), and the 1st International Workshop on Capability-oriented Business Informatics (CoBI 2014) co-located with the 16th IEEE International Conference on B, pp. 1–15 (2014)Google Scholar
  3. 3.
    Van Nuffel, D., Mulder, H., Van Kervel, S.: Enhancing the formal foundations of BPMN by enterprise ontology. In: Albani, A., Barjis, J., Dietz, J.L.G. (eds.) CIAO!/EOMAS. LNBIP, vol. 34, pp. 115–129. Springer, Heidelberg (2009). Scholar
  4. 4.
    Kitchenham, B., Charters, S.: Guidelines for performing Systematic Literature Reviews in Software Engineering (2007)Google Scholar
  5. 5.
    Vanderfeesten, I., Cardoso, J., Mendling, J., Reijers, H.A., Van Der Aalst, W.: Quality metrics for business process models. In: BPM and Workflow Handbook, pp. 1–12 (2007) Google Scholar
  6. 6.
    Reijers, H.A.: A cohesion metric for the definition of activities in a workflow process. In: Proceedings EMMSAD (2003). Accessed 01 Feb 2015
  7. 7.
    Fu, X., Zou, P., Ma, Y., Jiang, Y., Yue, K.: A control-flow complexity measure of web service composition process. In: Proceedings of 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010, pp. 712–716 (2010)Google Scholar
  8. 8.
    Cardoso, J., Mendling, J., Neumann, G., Reijers, H.A.: A discourse on complexity of process models. In: Eder, J., Dustdar, S. (eds.) BPM 2006. LNCS, vol. 4103, pp. 117–128. Springer, Heidelberg (2006). Scholar
  9. 9.
    Shao, J., Wang, Y.: A new measure of software complexity based on cognitive weights “Une nouvelle métrique de complexité logicielle basée sur les poids cognitifs,” October, vol. 28, no. 2, pp. 69–74 (2003)Google Scholar
  10. 10.
    Muketha, G.M., Abd Ghani, A.A., Selamat, M.H., Atan, R.: A survey of business process complexity metrics. Inf. Technol. J. 9, 1336–1344 (2010)CrossRefGoogle Scholar
  11. 11.
    Makni, L., Khlif, W., Haddar, N.Z., Ben-abdallah, H.: A tool for evaluating the quality of business process models overview on current metrics for BPM, pp. 230–242Google Scholar
  12. 12.
    Vanderfeesten, I., Cardoso, J., Reijers, H.A.: A weighted coupling metric for business process models. In: CEUR Workshop Proceedings, vol. 247, pp. 41–44 (2007)Google Scholar
  13. 13.
    Gruhn, V., Laue, R.: Adopting the cognitive complexity measure for business process models. In: 2006 5th IEEE International Conference on Cognitive Informatics, vol. 1, pp. 236–241 (2006)Google Scholar
  14. 14.
    Roy, S., Sajeev, A.S.M., Bihary, S., Ranjan, A.: An empirical study of error patterns in industrial business process models. IEEE Trans. Serv. Comput. 7(2), 140–153 (2014)CrossRefGoogle Scholar
  15. 15.
    Parizi, R.M., Ghani, A.A.A.: An ensemble of complexity metrics for BPEL web processes. In: Proceedings of 9th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing. SNPD 2008 2nd Int. Work. Adv. Internet Technol. Appl., pp. 753–758 (2008)Google Scholar
  16. 16.
    Rolón, E., Cardoso, J., García, F., Ruiz, F., Piattini, M.: Analysis and validation of control-flow complexity measures with BPMN process models. In: Halpin, T., et al. (eds.) BPMDS/EMMSAD. LNBIP, vol. 29, pp. 58–70. Springer, Heidelberg (2009). Scholar
  17. 17.
    Cardoso, J.: Business process control-flow complexity: metric, evaluation, and validation. Int. J. Web Serv. Res. 5, 49–76 (2008)CrossRefGoogle Scholar
  18. 18.
    Reijers, H.A., Vanderfeesten, Irene T.P.: Cohesion and coupling metrics for workflow process design. In: Desel, J., Pernici, B., Weske, M. (eds.) BPM 2004. LNCS, vol. 3080, pp. 290–305. Springer, Heidelberg (2004). Accessed 01 Feb 2015CrossRefGoogle Scholar
  19. 19.
    Gruhn, V., Laue, R.: Complexity metrics for business process models. In: 9th International Conference on Business Information Systems, pp. 1–12 (2006)Google Scholar
  20. 20.
    Azim, A., Ghani, A., Tieng, K., Geoffrey, W., Muketha, M., Wen, W.P.: Complexity metrics for measuring the understandability and maintainability of business process models using goal-question-metric (GQM). J. Comput. Sci. 8(5), 219–225 (2008)Google Scholar
  21. 21.
    Lassen, K.B., van der Aalst, W.M.P.: Complexity metrics for Workflow nets. Inf. Softw. Technol. 51(3), 610–626 (2009)CrossRefGoogle Scholar
  22. 22.
    Cardoso, J.: Control-flow complexity measurement of processes and Weyuker’s properties. In: 6th International Conference on Enformatika, vol. 8, no. 8, pp. 213–218 (2005)Google Scholar
  23. 23.
    Khlif, W., Zaaboub, N., Ben-Abdallah, H.: Coupling metrics for business process modeling. WSEAS Trans. Comput. 9, 31–41 (2010)Google Scholar
  24. 24.
    Vanderfeesten, I., Reijers, H.A., van der Aalst, W.M.P.: Evaluating workflow process designs using cohesion and coupling metrics. Comput. Ind. 59, 420–437 (2008)CrossRefGoogle Scholar
  25. 25.
    Solichah, I., Hamilton, M., Mursanto, P., Ryan, C., Perepletchikov, M.: Exploration on software complexity metrics for business process model and notation. In: 2013 International Conference on Advanced Computer Science and Information Systems, pp. 31–37 (2013)Google Scholar
  26. 26.
    Latva-Koivisto, A.M.: Finding a complexity measure for business process models. Complexity (2001). Accessed 01 Feb 2015
  27. 27.
    Cardoso, J.: How to measure the control-flow complexity of web process and workflows. In: Workflow Handbook 2005, pp. 199–212 (2005)Google Scholar
  28. 28.
    Huang, Z., Kumar, A.: New quality metrics for evaluating process models. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 164–170. Springer, Heidelberg (2009). Scholar
  29. 29.
    Vanderfeesten, I., Reijers, H.A., Mendling, J., van der Aalst, W.M.P., Cardoso, J.: On a quest for good process models: the cross-connectivity metric. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 480–494. Springer, Heidelberg (2008). Scholar
  30. 30.
    Mendling, J., Neumann, G., Van Der Aalst, W.M.P.: On the correlation between process model metrics and errors. In: 26th International Conference on Conceptual Modeling, pp. 173–178 (2007)Google Scholar
  31. 31.
    Cardoso, J.: Process control-flow complexity metric: an empirical validation. In: Proceedings - 2006 IEEE International Conference on Services Computing, SCC 2006, pp. 167–173 (2006)Google Scholar
  32. 32.
    Kluza, K., Nalepa, G.J.: Proposal of square metrics for measuring business process model complexity. In: Federated Conference on Computer Science and Information Systems, pp. 919–922 (2012)Google Scholar
  33. 33.
    Khlif, W., Makni, L.: Quality metrics for business process modeling. In: Proceedings of the 9th WSEAS International Conference on Applied Computer Science, vol. 9, no. 1, pp. 195–200 (2009)Google Scholar
  34. 34.
    Henry, S., Kafura, D.: Software structure metrics based on information flow. IEEE Trans. Softw. Eng. SE-7(5), 510–518 (1981)CrossRefGoogle Scholar
  35. 35.
    Thammarak, K.: Survey complexity metrics for reusable business process. In: National Conference on Applied Computer Technology and Information System, pp. 18–22 (2010)Google Scholar
  36. 36.
    Mendling, J.: Testing density as a complexity metric for EPCs. In: Analysis (2006)Google Scholar
  37. 37.
    Sánchez-González, L., Ruiz, F., García, F., Cardoso, J.: Towards thresholds of control flow complexity measures for BPMN models. In: Proceedings of the 2011 ACM Symposium on Applied Computing, pp. 1445–1450 (2011)Google Scholar
  38. 38.
    Boehm, B., Clark, B., Horowitz, E., Westland, Ch., Madachy, R., Selby, R.: Cost models for future software life cycle processes: COCOMO 2.0. Ann. Softw. Eng. 1(1), 57–94 (1995)CrossRefGoogle Scholar
  39. 39.
    Pavlicek, J.: The estimation of managerial characteristics of IS development in the stage of requirements specification, Pavlicek Ph.D. work, CULS-2006Google Scholar
  40. 40.
    Pavlicek, J., Hronza, R., Pavlickova, P.: Educational business process model skills improvement. In: Pergl, R., Molhanec, M., Babkin, E., Fosso Wamba, S. (eds.) EOMAS 2016. LNBIP, vol. 272, pp. 172–184. Springer, Cham (2016). ISBN 978-3-319-49454-8CrossRefGoogle Scholar
  41. 41.
    Pavlicek, J., Hronza, R., Pavlickova, P., Jelinkova, K.: The business process model quality metrics. In: Pergl, R., Lock, R., Babkin, E., Molhanec, M. (eds.) EOMAS 2017, vol. 298, pp. 134–148. Springer, Cham (2017). ISBN 978-3-319-681849CrossRefGoogle Scholar
  42. 42.
    Pavlicek, J., Pavlickova, P.: Methods for evaluating the quality of process modelling tools. In: Pergl, R., Babkin, E., Lock, R., Malyzhenkov, P., Merunka, V. (eds.) EOMAS 2018. LNBIP, vol. 332, pp. 171–177. Springer, Cham (2018). ISBN 978-3-030-00787-4CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Josef Pavlicek
    • 1
    Email author
  • Petra Pavlickova
    • 1
  • Pavel Naplava
    • 2
  1. 1.Faculty of Information TechnologyCTUPragueCzech Republic
  2. 2.Faculty of Electrical EngineeringCTUPragueCzech Republic

Personalised recommendations