Structuredness and its significance for correctness of process models

Abstract

Recent research has shown that business process models from practice suffer from several quality problems. In particular, the correctness of control flow has been analyzed for industry-scale collections of process models revealing that error ratios are surprisingly high. In the past the structuredness property has been discussed as a guideline to avoid errors, first in research on programming, and later also in business process modeling. In this paper we investigate the importance of structuredness for process model correctness from an empirical perspective. We introduce definitions of two metrics that quantify the (un)structuredness of a process model, the degree of structuredness and the unmatched connector count. Then, we use the event-driven process chain models of the SAP reference model for validating the capability of these metrics to predict error probability. Our findings clearly support the importance of structuredness as a design principle for achieving correctness in process models.

Understanding Business Process Models: The Costs and Benefits of Structuredness

“…As a result of a study on predicting error probability for a set of 2,000 process models from practice [20], structuredness appears to be the best determinant to distinguish low-error-probability models from ones with high error probability. Another study confirms the significance of structuredness, albeit that different definitions are used [13]. These and other experiments are summarized in the seven process modeling guidelines [19].…”

“…It is possible that the lack of node labels referring to a real-world application domain might have an effect on the results of the study. The latter limitation is shared with several other studies on complexity and understandability [13,28,30].…”

Making sense of business process descriptions: An experimental comparison of graphical and textual notations

“…It has to be noted for this set of experiments that several modelrelated factors were controlled. In other works it has been shown that whether the information in the model is well organized in terms of labeling [42], secondary notation [59], iconic symbol design [67,45], or structuredness [33] has an important influence on understanding. In the same vein, a variation in complexity of the process model in terms of size and other metrics [7,24,43,38] and individual differences [60] results in different levels of understanding.…”

Managing Process Model Complexity Via Abstract Syntax Modifications

“…• model size, the summed size of all modules in a process model [76]; • repository size, the summed size of all models in a process model repository; • models, the number of models in a process model repository [117]; • depth, the number of modular levels appearing in a process model [117]; • diameter, the longest path from a start to an end element in a process model [76]; • average gateway degree, the number of nodes a gateway in a specific process model is on average connected to [76]; • structuredness, the restructuring ratio of an unstructured model to a structured variant of it [69]; • modules overhead, the ratio between modules and model size; • fan-in, the average number of references to a module [67]. For example, Fig.…”

“…Metrics Increases structuredness of a process model. Rationale Structured models are easier to understand [80], [81] and less error-prone [76], [69] than unstructured models. Realization The problem of structuring process models has been extensively analyzed in the literature both from an empirical and from a theoretical point of view.…”