Document Server@UHasselt >
Faculty of Business Economics >
Master theses >
Please use this identifier to cite or link to this item:
|Title: ||Comprehension of Business Process Models: An evaluation of metrics for understandability|
|Authors: ||Van Eijk, Koen|
|Advisors: ||DEPAIRE, Benoit|
|Issue Date: ||2015|
|Abstract: ||For a business it is crucial that all parties can participate in all the stages of business process design. Not only the way a business is currently working, but also the way it is supposed to work are important topics that need to be communicated between all parties involved and this underlines the importance of understandability of business process models (BPM). Understandability can be linked to the complexity of BPM. This master's thesis focusses on the cognitive complexity of BPM.
The literature concerning BPM understandability has already adapted numerous metrics from software engineering. These metrics seem to measure different aspects of cognitive and structural complexity. This master's thesis tries to identify useful combinations of metrics. Better metrics should lead to better conclusions, which should lead to better communication of BPM.
Van der Aalst's Workflow Patterns are used as a basis for comparing the different metrics. They are expressed using the BPMN notation and the complexity level for the different metrics is computed. It was found that the different metrics are not equally sensitive. They do however give the same ranking to the patterns with regards to their complexity. By analyzing the data it was found that the metrics address two aspects of complexity. On the one side some metrics address structural complexity and on the other side some metrics address functional complexity. It is concluded that it is preferable to use combinations of metrics that together address both aspects.|
|Notes: ||Master of Management-Management Information Systems|
|Type: ||Theses and Dissertations|
|Appears in Collections: ||Master theses|
Files in This Item:
|N/A||1.65 MB||Adobe PDF|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.