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Thresholds for error probability measures of business process models

Mendling, Jan, Sanchez-Gonzalez, Laura , Garcia, Felix , & La Rosa, Marcello (2012) Thresholds for error probability measures of business process models. Journal of Systems and Software, 85(5), pp. 1188-1197.

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Abstract

The quality of conceptual business process models is highly relevant for the design of corresponding information systems. In particular, a precise measurement of model characteristics can be beneficial from a business perspective, helping to save costs thanks to early error detection. This is just as true from a software engineering point of view. In this latter case, models facilitate stakeholder communication and software system design. Research has investigated several proposals as regards measures for business process models, from a rather correlational perspective. This is helpful for understanding, for example size and complexity as general driving forces of error probability. Yet, design decisions usually have to build on thresholds, which can reliably indicate that a certain counter-action has to be taken. This cannot be achieved only by providing measures; it requires a systematic identification of effective and meaningful thresholds. In this paper, we derive thresholds for a set of structural measures for predicting errors in conceptual process models. To this end, we use a collection of 2,000 business process models from practice as a means of determining thresholds, applying an adaptation of the ROC curves method. Furthermore, an extensive validation of the derived thresholds was conducted by using 429 EPC models from an Australian financial institution. Finally, significant thresholds were adapted to refine existing modeling guidelines in a quantitative way.

Impact and interest:

8 citations in Scopus
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2 citations in Web of Science®

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ID Code: 47988
Item Type: Journal Article
Keywords: process model, error probability, threshold, ROC curves, repository
DOI: 10.1016/j.jss.2012.01.017
ISSN: 0164-1212
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Past > Schools > Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Elsevier.
Copyright Statement: This is the author’s version of a work that was accepted for publication in Journal of Systems and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Systems and Software, [VOL 85, ISSUE 5, (2013)] DOI: 10.1016/j.jss.2012.01.017
Deposited On: 10 Jan 2012 10:33
Last Modified: 12 Sep 2013 12:21

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