Thresholds for error probability measures of business process models

  • Authors:

  • Jan Mendling;Laura Sánchez-González;Félix García;Marcello La Rosa

  • Affiliations:

  • Wirtschaftsuniversität Wien, Augasse 2-6, 1090 Vienna, Austria;Alarcos Research Group, TSI Department, University of Castilla La Mancha, Paseo de la Universidad, no4, 13071 Ciudad Real, Spain;Alarcos Research Group, TSI Department, University of Castilla La Mancha, Paseo de la Universidad, no4, 13071 Ciudad Real, Spain;Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia and NICTA Queensland Lab, PO Box 6020, St Lucia, QLD 4067, Australia

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2012

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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 2000 business process models from practice as a means of determining thresholds, applying an adaptation of the ROC curve 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.