Information Technology Reference
In-Depth Information
diculty. Implications of these results for researchers include exercising caution
when aggregating answering rates of randomly chosen comprehension questions
to total comprehension measures for models. As the choice of questions might
significantly influence comprehension scores, balanced selection and construction
of questions is highly relevant.
In addition, our work provides further evidence that high interactivity of
elements may heighten cognitive load and lower comprehensibility of BPM. If
possible, deep nesting of control-flow blocks should be avoided in order to make
understanding easier and - in the end - to improve the quality of BPM and
reduce modeling errors. Research on modularity of BPM [1] suggests that de-
composing complex models into smaller submodels can improve model compre-
hensibility. Additionally syntax highlighting [29] can be used to heighten com-
prehensibility of deeply nested blocks.
Future research is needed to determine valid and reliable values for the cogni-
tive diculty of understanding specific relations between model elements. These
values could make it possible to finally estimate understandability of models
without the need of a user evaluation. Looking ahead, exact comprehension val-
ues could then be used to guide modeling tool developers to provide feedback on
cognitive diculty of models to users or to give hints on possible understand-
ability problems in models.
References
1. Reijers, H., Mendling, J.: Modularity in process models: Review and effects. In:
Proc. of the 6th Int. Conf. on Business Process Management, pp. 20-35. Springer,
Heidelberg (2008)
2. Mendling, J., Strembeck, M.: Influence factors of understanding business process
models. In: Schlender, B., Frielinghaus, W. (eds.) Business Information Systems.
LNBIP, vol. 7, pp. 142-153. Springer, Heidelberg (1974)
3. Genon, N., Heymans, P., Moody, D., Amyot, D.: Improving the cognitive effective-
ness of the bpmn 2.0 visual syntax (2010)
4. Gruhn, V., Laue, R.: Complexity metrics for business process models. In: 9th Inter-
national Conference on Business Information, pp. 1-12. Springer, Heidelberg (2006)
5. Mendling, J.:Metrics for Process Models: Empirical Foundations of Verification,
Error Prediction, and Guidelines for Correctness. LNBIP, vol. 6. Springer, Heidel-
berg (1974)
6. Gonzalez, L.S., Rubio, F.G., Gonzalez, F.R., Velthuis, M.P.: Measurement in busi-
ness processes: a systematic review. Business Process Management Journal 16,
114-134 (2010)
7. Mendling, J., Reijers, H.A., Cardoso, J.: What makes process models understand-
able? In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714,
pp. 48-63. Springer, Heidelberg (2007)
8. Reijers, H., Mendling, J.: A study into the factors that influence the understand-
ability of business process models. IEEE Transactions on Systems, Man, and
Cybernetics, Part A (2010)
9. Aranda, J., Ernst, N., Horkoff, J., Easterbrook, S.: A framework for empirical eval-
uation of model comprehensibility. In: MISE 2007: Proceedings of the International
Workshop on Modeling in Software Engineering (2007)
