Translating unstructured workflow processes to readable BPEL: Theory and implementation

et al. 2011

Computers in Industry

187

120

With the increasing adoption of process-aware information systems, large process model repositories have emerged. Typically, the models in such repositories are re-aligned to real-world events and demands through adaptation on a day-to-day basis. This bears the risk of introducing model redundancies and of unnecessarily increasing model complexity. If no continuous investment is made in keeping process models simple, changes will become more difficult and error-prone over time. Although refactoring techniques are widely used in software engineering to address similar problems, so far, no comparable state-of-the-art has evolved in the business process management domain. Process designers either have to refactor process models by hand or are simply unable to apply respective techniques at all. This paper proposes a catalogue of process model "smells" for identifying refactoring opportunities. In addition, it introduces a set of behavior-preserving techniques for refactoring large process repositories. The proposed refactorings enable process designers to effectively deal with model complexity by making process models better understandable and easier to maintain. The refactorings have been evaluated using large process repositories from the healthcare and automotive domain. To demonstrate the feasibility of the refactoring techniques, a proof-of-concept prototype has been implemented.

Section: Smell Sources Literaturementioning

confidence: 99%

Section: Pms2: Contrived Complexitymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Refactoring large process model repositories

Weber

Reichert

et al. 2011

Computers in Industry

187

120

“…Metrics including size, complexity and coupling are validated for their correlation with understanding and maintainability [31,32]. Further metrics aim to quantify cognitive complexity and modularity [33,34,35,36]. Various metrics have been validated as predictors of error probability [37], which is assumed to be a symptom of bad understanding by the modeler during the process of model creation.…”

Section: Structural Factors Of Process Model Understandingmentioning

confidence: 99%

Managing Structural and Textual Quality of Business Process Models

Lecture Notes in Business Information Processing

2013

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.

“…This work has been used to eliminate OR-joins from process models [56]. Further research discusses the untangling of unstructured loops and the derivation of structured BPEL from arbitrary Petri nets [57,58]. A desirable contribution would be to change a process model in such a way that the behavior is kept, but the structure optimized, e.g.…”

Section: Implications For Future Researchmentioning

confidence: 99%

Empirical Studies in Process Model Verification

Transactions on Petri Nets and Other Models of Concurrency II

2009

Abstract. Despite the large body of knowledge on formal analysis techniques for process models, in particular Petri nets, there has been a notable gap of empirical research into verification. In this paper we compare the few studies that report results from applying verification techniques to real-world process model collections. For this comparison we are particularly interested in the different approaches, their computational performance, and the number of errors found. Our comparison reveals that most of the samples have error rates of 10% to 20%. Some of the studies have established a connection between error probability and process model metrics, as well as between model understanding and both metrics and modeling competence of the model reader. Based on these results, we discuss implications and directions for future research.