Empirical Studies in Process Model Verification

Journal of Systems and Software

Sánchez-González

García

et al. 2012

Self Cite

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.

Section: Business Process Models and Errorsmentioning

confidence: 99%

Thresholds for error probability measures of business process models

Journal of Systems and Software

Sánchez-González

García

et al. 2012

Self Cite

“…Maintenance and quality assurance of these process models is a critical challenge given this big number of models and modelers. Indeed, different quality issues have been observed: up to 20% of all models in collections from practice contain errors [2], and many casual modelers are not sufficiently trained [3].…”

Section: Introductionmentioning

confidence: 99%

On the refactoring of activity labels in business process models

Leopold

Smirnov

2012

Information Systems

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Large corporations increasingly utilize business process models for documenting and redesigning their operations. The extent of such modeling initiatives with several hundred models and dozens of often hardly trained modelers calls for automated quality assurance. While formal properties of control flow can easily be checked by existing tools, there is a notable gap for checking the quality of the textual content of models, in particular, its activity labels. In this paper, we address the problem of activity label quality in business process models. We designed a technique for the recognition of labeling styles, and the automatic refactoring of labels with quality issues. More specifically, we developed a parsing algorithm that is able to deal with the shortness of activity labels, which integrates natural language tools like WordNet and the Stanford Parser. Using three business process model collections from practice with differing labeling style distributions, we demonstrate the applicability of our technique. In comparison to a straightforward application of standard natural language tools, our technique provides much more stable results. As an outcome, the technique shifts the boundary of process model quality issues that can be checked automatically from syntactic to semantic aspects.

“…In practice a considerable percentage of process models has quality issues, with often 5% to 30% of the models having problems with soundness [9]. The reason for at least some of these issues is the growth of many process modeling initiatives.…”

Section: Introductionmentioning

confidence: 99%

Managing Structural and Textual Quality of Business Process Models

Lecture Notes in Business Information Processing

2013

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Abstract. Business process models are increasingly used for capturing business operations of companies. Such models play an important role in the requirements elicitation phase of to-be-created information systems and in as-is analysis of business efficiency. Many process modeling initiatives have grown considerably big in size involving dozens of modelers with varying expertise creating and maintaining hundreds, sometimes thousands of models. One of the roadblocks towards a more effective usage of these process models is the often insufficient provision of quality assurance. The aim of this paper is to give an overview on how empirical research informs structural and textual quality assurance of process models. We present selected findings and show how they can be utilized as a foundation for novel automatic analysis techniques.