Evaluating modeling method - Modeling method

Part 2 - Analyzing weak predictors

9.2 Modeling method

9.2.2 Evaluating modeling method

A first thing to note is that the decision is made to stop adding more detail although there still is more information present about certain components in the case description, like for the components

“inspect” and “arrange taxiing”. Although in this situation only for some components there is more information present, in real life it could be that for all or other components was more information.

Therefore would it for real life situations be even more helpful to determine the preferred level of detail on beforehand. Otherwise it would be very hard to determine at what point to stop de-aggregating and making it more concrete.

Furthermore might this way of working seem like a hassle. It seems easier or at least a lot faster to immediately start modeling, especially with a case description at hand. However, this temptation is also present in creating a BISA. In real life this temptation in creating a BISA is probably more dominantly present than for process modeling. This because, all the legacy systems are known from the beginning to the most disaggregated, concrete and realized situation. Therefore an architect could start with one legacy system and draw from there all relations to the other information systems on the same concrete, disaggregated and realized level. However, in that field still the described top-down method is introduced. Besides that, from our point of view it is much harder to even try to start modeling immediately in real life. Typically one will get to know the process, starting with the big picture and from there going into more detail, which matches the proposed modeling method instead of immediately starting at the level of detail that is preferred for the end product.

The constructed method is thought to be an operationalization of the rationale because this method takes into account and omits potential problems of bounded rationality and limited information processing capacity. This because, at every moment in time only a small part of all available information needs to be considered and there are less and smaller decisions to be made at every point through the creation process to finish a step and go on to the next step. The so called predictor could be “used modeling method” where this method is thought to be a good method. Like for the predictor labeling style verb-object is thought to be the best style, is this method thought to be a good method.

There is one large limitation to this method at this point in time. There is no empirical evidence supporting the quality of this method. Due to a lack of time and resources it was not possible to empirically investigate this method. It would be very valuable to test this method before applying or discarding it. A first test could be to run a similar experiment as was done for the data in this thesis, but first introduce the modeling method and then let them create the NFL-case and the PF-case applying the new method. The results could be compared between the current set of data and the data that is constructed throughout the new method.

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http://link.springer.com/chapter/10.1007/978-3-642-16934-2_9

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Appendix A - Found literature based on snowballing

The search for literature about process model quality started with a set of eight papers:

(Cardoso 2006), (Claes et al., n.d.), (Laue & Mendling, 2010), (Maes & Poels, 2007), (D. L. Moody, 2005), (Nelson, Poels, Genero, & Piattini, 2011), (Reijers and Mendling 2011) and (Schrepfer, Wolf, Mendling, & Reijers, 2009).

Forward snowballing was applied until februari 2013, since that was the starting point of the search for literature. The snowballing resulted in the following set of papers, the ones that are greyed out are the ones that are eventually excluded from the used set.

Aalst, W. Van Der, & Hofstede, A. Ter. (2000). Verification of workflow task structures: A

petri-net-baset approach. Information systems. Retrieved from

http://www.sciencedirect.com/science/article/pii/S0306437900000089

Aalst, W. M. P., Hee, K. M., Hofstede, a. H. M., Sidorova, N., Verbeek, H. M. W., Voorhoeve, M., &

Wynn, M. T. (2010). Soundness of workflow nets: classification, decidability, and analysis.

Formal Aspects of Computing, 23(3), 333–363. doi:10.1007/s00165-010-0161-4

Agarwal, R., De, P., & Sinha, a. P. (1999). Comprehending object and process models: an empirical study. IEEE Transactions on Software Engineering, 25(4), 541–556. doi:10.1109/32.799953 Aguilar, E., Garcıa, F., Ruiz, F., & Piattini, M. (2007). An exploratory experiment to validate

measures for business process models. First international conference on research …, (1), 271–

280. Retrieved from

http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:An+Exploratory+Experiment +to+Validate+Measures+for+Business+Process+Models#0

Assessing the Impact of Hierarchy on Model Understandability – A Cognitive Perspective. (n.d.).

Bandara, W. Gable, G., Rosemann, M. (2007). Critical Success Factors of Business Process Modeling, 33. Retrieved from http://eprints.qut.edu.au

Becker, J., Rosemann, M., & Uthmann, C. Von. (2000). Guidelines of Business Process Modeling, 30–49.

Brito e Abreu, F., da Porciuncula, R. D. B. V., Freitas, J. M., & Costa, J. C. (2010). Definition and Validation of Metrics for ITSM Process Models. 2010 Seventh International Conference on the Quality of Information and Communications Technology, 79–88. doi:10.1109/QUATIC.2010.13 Canfora, G., García, F., Piattini, M., Ruiz, F., & Visaggio, C. a. (2005). A family of experiments to

validate metrics for software process models. Journal of Systems and Software, 77(2), 113–129.

doi:10.1016/j.jss.2004.11.007

Cardoso, J, Mendling, J., Neumann, G., & Reijers, H. A. (2006). A Discourse on Complexity of Process Models, 115–126.

Cardoso, Jorge. (2005). Control-flow Complexity Measurement of Processes and Weyuker ’ s Properties, 213–218.

Cardoso, Jorge. (2006). Process control-flow complexity metric : An empirical validation, 167–173.

Claes, J., Vanderfeesten, I., Reijers, H. A., Pinggera, J., Weidlich, M., Fahland, D., … Poels, G. (n.d.).

An Exploration of the Relationship between Modeling Behavior and Process Model Quality.

Claes, J., Vanderfeesten, I., Reijers, H. A., Pinggera, J., Weidlich, M., Zugal, S., … Mendling, J.

(2012). Tying Process Model Quality to the Modeling Process : The Impact of Structuring , Movement , and Speed Background on the Process of Process Modeling, 1–16.

Conference, I., Approaches, N., & Engineering, S. (n.d.). No Title.

Cruz-Lemus, J. a., Maes, A., Genero, M., Poels, G., & Piattini, M. (2010). The impact of structural complexity on the understandability of UML statechart diagrams. Information Sciences, 180(11), 2209–2220. doi:10.1016/j.ins.2010.01.026

Dumas, M., Rosa, M. La, Mendling, J., & Raul, M. (2012). Understanding Business Process Models : The Costs and Benefits of Structuredness, 1–16.

Esparza, J. (1996). Reachability in Live and Safe Free-Choice Petri Nets is NP-complete.

Figl, K., Recker, J., & Mendling, J. (2013). A study on the effects of routing symbol design on process model comprehension. Decision Support Systems, 54(2), 1104–1118.

doi:10.1016/j.dss.2012.10.037

González, L. S., Rubio, F. G., González, F. R., & Velthuis, M. P. (2010). Measurement in business processes: a systematic review. Business Process Management Journal, 16(1), 114–134.

doi:10.1108/14637151011017976

Green, T. M., Ribarsky, W., & Fisher, B. (2009). Building and applying a human cognition model for visual analytics. Information Visualization, 8(1), 1–13. doi:10.1057/ivs.2008.28

Green, T. R. G., & Petre, M. (1996). Usability Analysis of Visual Programming Environments: A

“Cognitive Dimensions” Framework. Journal of Visual Languages & Computing, 7(2), 131–

174. doi:10.1006/jvlc.1996.0009

Gruhn, V., & Laue, R. (2007). What business process modelers can learn from programmers. Science of Computer Programming, 65(1), 4–13. doi:10.1016/j.scico.2006.08.003

Houy, C., Fettke, P., & Loos, P. (2012). Understanding Understandability of Conceptual Models – What Are We Actually Talking about ?, 64–77.

Huang, Z., & Kumar, A. (2009). New Quality Metrics for Evaluating Process Models, 1(d), 164–170.

Krogstie, J., Sindre, G., & Jørgensen, H. (2006). Process models representing knowledge for action: a revised quality framework. European Journal of Information Systems, 15(1), 91–102.

doi:10.1057/palgrave.ejis.3000598

Latva-Koivisto, A. M. (2001). Finding a complexity measure for business process models, 1–26.

Laue, R, & Mendling, J. (2008). The impact of structuredness on error probability of process models.

Information Systems and e-Business Technologies. Retrieved from http://link.springer.com/chapter/10.1007/978-3-540-78942-0_56

Laue, Ralf, & Gadatsch, A. (2011). Measuring the Understandability of Business Process Models - Are We Asking the Right Questions ?

Laue, Ralf, & Mendling, Æ. J. (2010). Structuredness and its significance for correctness of process models, 287–307. doi:10.1007/s10257-009-0120-x

Lee, G. S., & Yoon, J.-M. (1990). An empirical study on complexity metrics of Petri nets.

Microelectronics Reliability, 32(9), 1215–1221. doi:10.1016/0026-2714(92)90643-Y

Lindland, O., Sindre, G., & Solvberg, A. (1994). Understanding quality in conceptual modeling.

Software, IEEE, 42–49. Retrieved from

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=268955

Maes, A., & Poels, G. (2007). Evaluating quality of conceptual modelling scripts based on user perceptions, 63, 701–724. doi:10.1016/j.datak.2007.04.008

Melcher, J., Mendling, J., Reijers, H. A., & Seese, D. (2009). On Measuring the Understandability of Process Models ( Experimental Results ).

Mendling, J, Reijers, H. A., & Cardoso, J. (2007). What Makes Process Models Understandable ?, 1–

16.

Mendling, J, Verbeek, H. M. W., & Dongen, B. F. Van. (2007). Detection and Prediction of Errors in EPCs of the SAP Reference Model, (July 2007).

Mendling, J., Reijers, H. a., & Recker, J. (2010). Activity labeling in process modeling: Empirical insights and recommendations. Information Systems, 35(4), 467–482.

doi:10.1016/j.is.2009.03.009

Mendling, J., Reijers, H. a., & van der Aalst, W. M. P. (2010). Seven process modeling guidelines (7PMG). Information and Software Technology, 52(2), 127–136.

doi:10.1016/j.infsof.2009.08.004

Mendling, Jan, & Neumann, G. (2007). Error Metrics for Business Process Models. CAiSE Forum, 53–56. Retrieved from http://ww.w.mendling.com/publications/07-CAISEFORUM.pdf

Mendling, Jan, Neumann, G., & Aalst, W. Van Der. (2007). Understanding the Occurrence of Errors in Process Models based on Metrics.

Mendling, Jan, Sánchez-González, L., García, F., & La Rosa, M. (2012). Thresholds for error probability measures of business process models. Journal of Systems and Software, 85(5), 1188–

1197. doi:10.1016/j.jss.2012.01.017

Mendling, Jan, & Strembeck, M. (2008). Influence factors of understanding business process models.

Business Information Systems. Retrieved from http://link.springer.com/chapter/10.1007/978-3-540-79396-0_13

Mendling, Jan, Strembeck, M., & Recker, J. (2012). ePub WU Institutional Repository Factors of Process Model Comprehension - Findings from a Series of Experiments.

Meyer, J., Thomas, J., Diehl, S., Fisher, B., Keim, D., Laidlaw, D., … Ynnerman, A. (2007). From Visualization to Visually Enabled Reasoning.

Moody, D, Sindre, G., Brasethvik, T., & Sølvberg, A. (2003). Evaluating the quality of process models: Empirical testing of a quality framework. In Conceptual Modeling—ER … (pp. 380–

396). Retrieved from http://www.springerlink.com/index/4mda6mk1d9kmcnaj.pdf

Moody, D. L. (2005). Theoretical and practical issues in evaluating the quality of conceptual models:

current state and future directions. Data & Knowledge Engineering, 55(3), 243–276.

doi:10.1016/j.datak.2004.12.005

Moody, D. L. (2009). Notations in Software Engineering, 35(5), 756–778.

Nelson, H. J., Poels, G., Genero, M., & Piattini, M. (2011). A conceptual modeling quality framework. Software Quality Journal, 20(1), 201–228. doi:10.1007/s11219-011-9136-9

Petre, M. (1995). Why looking isn’t always seeing: readership skills and graphical programming.

Communications of the ACM, 38(6), 33–44. doi:10.1145/203241.203251

Pinggera, J., Furtner, M., Martini, M., Sachse, P., Zugal, S., & Weber, B. (n.d.). Investigating the Process of Process Modeling with Eye Movement Analysis.

Purchase, H. (1997). Which Aesthetic Has the Greatest Effect on Human Understanding ?

Recker, J., & Dreiling, A. (2007). Does It Matter Which Process Modelling Language We Teach or Use ? An Experimental Study on Understanding Process Modelling Languages without Formal Education, 356–366.

Reijers, H. a., Freytag, T., Mendling, J., & Eckleder, A. (2011). Syntax highlighting in business process models. Decision Support Systems, 51(3), 339–349. doi:10.1016/j.dss.2010.12.013 Reijers, H. a., Mendling, J., & Dijkman, R. M. (2011). Human and automatic modularizations of

process models to enhance their comprehension. Information Systems, 36(5), 881–897.

doi:10.1016/j.is.2011.03.003

Rolon, E., Sanchez, L., Garcia, F., Ruiz, F., Piattini, M., Caivano, D., & Visaggio, G. (2009).

Prediction Models for BPMN Usability and Maintainability. 2009 IEEE Conference on Commerce and Enterprise Computing, 383–390. doi:10.1109/CEC.2009.53

Rosemann, M. (2006a). Potential pitfalls of process modeling: part A. Business Process Management Journal, 12(2), 249–254. doi:10.1108/14637150610657567

Rosemann, M. (2006b). Potential pitfalls of process modeling: part B. Business Process Management Journal, 12(3), 377–384. doi:10.1108/14637150610668024

Sánchez-González, L, & García, F. (2010). Quality assessment of business process models based on

thresholds. On the Move to …, 1–2. Retrieved from

http://link.springer.com/chapter/10.1007/978-3-642-16934-2_9

Schrepfer, M., Wolf, J., Mendling, J., & Reijers, H. A. (2009). The Impact of Secondary Notation on Process Model Understanding, 1–15.

Soffer, P., Kaner, M., & Wand, Y. (2012). Towards Understanding the Process of Process Modeling :, 357–369.

Vanderfeesten, I., Reijers, H. A., Mendling, J., Aalst, W. M. P. Van Der, & Cardoso, J. (2008). On a Quest for Good Process Models : The Cross-Connectivity Metric.

Appendix B - Literature search on key words

Used key words:

- Business process models (+ separability/ sequentiality/ error detection) - process models + separability/ sequentiality

- Workflow nets + separability/ sequentiality - separability+ sequentiality

- error metrics + separability/ sequentiality - error probability + separability/ sequentiality

- Connector heterogeneity + (business) process model/ process model vericifaction/ Workflow nets/ error probability

- Connector interplay + (business) process model/ process model vericifaction/ Workflow nets/

error probability

- Density + (business) process model/ process model vericifaction/ Workflow nets/ error probability

- Secondary notation+ (business) process model/ process model vericifaction/ Workflow nets/

error probability

- Arc intersection + (business) process model/ process model vericifaction/ Workflow nets/

error probability

- Crossing edges + (business) process model/ process model vericifaction/ Workflow nets/ error probability

Appendix C - Overview of hypotheses

Before the hypotheses are analyzed an overview of the all the hypotheses is shown in figure 24. The circles in the figure represent points where arrows are merged into one arrow. For example, at the top left the arrow from the block separability and the arrow from the block CH merge into one arrow pointing towards soundness, this arrow has the label “1b”. This shows that hypothesis 1b is about the predictive relation between separability and soundness in relation to CH.

In document Eindhoven University of Technology MASTER A framework for business process model quality and an evaluation of model characteristics as predictors for quality van Mersbergen, M. (pagina 58-88)