Prediction Models for BPMN Usability and Maintainability.
ABSTRACT The measurement of a business process in the early stages of the lifecycle, such as the design and modelling stages, could reduce costs and effort in future maintenance tasks. In this paper we present a set of measures for assessing the structural complexity of business processes models at a conceptual level. The aim is to obtain useful information about process maintenance and to estimate the quality of the process model in the early stages. Empirical validation of the measures was carried out along with a linear regression analysis aimed at estimating process model quality in terms of modifiability and understandability.
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Conference Proceeding: Error Metrics for Business Process Models.[show abstract] [hide abstract]
ABSTRACT: Little research has been conducted so far on causes for errors in business process models. In this paper we investigate on how mainly domain independent factors such as the size or complexity of models influence errors observed in a wide range of existing business process models. In particular, we provide a set of six metrics presumably related to the comprehensibility of both the process model structure and the process state space, and discuss their capability to predict errors in the SAP reference model. The results show that already the three metrics size, separability, and structuredness suffice to achieve a high Nagelkerke R2 value of 0.853 demonstrating a good predictive efficacy.CAiSE'07 Forum, Proceedings of the CAiSE'07 Forum at the 19th International Conference on Advanced Information Systems Engineering, Trondheim, Norway, 11-15 June 2007; 01/2007
Conference Proceeding: On a Quest for Good Process Models: The Cross-Connectivity Metric.Advanced Information Systems Engineering, 20th International Conference, CAiSE 2008, Montpellier, France, June 16-20, 2008, Proceedings; 01/2008
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ABSTRACT: Although software measurement plays an increasingly important role in Software Engineering, there is no consensus yet on many of the concepts and terminology used in this field. Even worse, vocabulary conflicts and inconsistencies can be frequently found amongst the many sources and references commonly used by software measurement researchers and practitioners. This article presents an analysis of the current situation, and provides a comparison framework that can be used to identify and address the discrepancies, gaps, and terminology conflicts that current software measurement proposals present. A basic software measurement ontology is introduced, that aims at contributing to the harmonization of the different software measurement proposals and standards, by providing a coherent set of common concepts used in software measurement. The ontology is also aligned with the metrology vocabulary used in other more mature measurement engineering disciplines.Information and Software Technology. 08/2006;