The Impact of Hierarchy on the Understandability of Process Models

The use of modularization to hierarchically structure information has for decades been identified as a viable approach to deal with complexity [1]. Not surprisingly, many conceptual modeling languages provide support for hierarchical structures, such as sub-processes in business process modeling languages like BPMN and YAWL or composite states in UML statecharts. While hierarchical structures have been recognized as an important factor influencing model understandability [2, 3], there are no definitive guidelines on their use yet. For instance, for business process models, recommendations for the size of a sub-process, i.e., sub-model, range from 5-7 model elements [4] over 5-15 model elements [5] to up to 50 model elements [6]. Also in empirical research into conceptual models (e.g., ER diagrams or UML statecharts) the question of whether and when hierarchical structures are beneficial for model understandability seems not to be entirely clear. While it is common belief that hierarchy has a positive influence on the understandability of a model, reported
data seems often inconclusive or even contradictory, cf. [7, 8].

As suggested by existing empirical evidence, hierarchy is not beneficial by default [9] and can even lead to performance decrease [7]. In the following, we will have a detailed look at which factors cause such discrepancies between the common belief in positive effects of hierarchy and reported data. In particular, we draw on concepts from cognitive psychology to develop a framework that describes how the impact of hierarchy on model understandability can be assessed. The contribution of this theoretical discussion is a perspective to disentangle the diverse findings from prior experiments.

 

Abstraction

Through the introduction of hierarchy it is possible to group a part of a model into a sub-model. When referring to such a sub-model, its content is hidden by providing an abstract description, such as a complex activity in a business process model or a composite state in an UML statechart. The concept of abstraction is far from new and known since the 1970s as  “information hiding”‘ [1]. In the context of our work, it is of interest in how far abstraction influences model understandability. From a theoretical point of view, abstraction should show a positive influence, as abstraction reduces the amount of elements that have to be considered simultaneously, i.e., abstraction can hide irrelevant information, cf. [10]. However, if positive effects depend on whether information can be hidden, the way how hierarchy is displayed apparently plays an important role. Here, we assume, similar to [10], that each sub-model is presented separately. In other words, each sub-model is displayed in a separate window if viewed on a computer, or printed on a single sheet of paper. The reader may arrange the sub-models according to her preferences and may close a window or put away a paper to hide information. To illustrate the impact of abstraction,consider the BPMN model shown in Figure 1. Assume the reader wants to determine whether the model allows for the execution of sequence A, B, C. Through the abstraction introduced by sub-processes A and C, the reader can answer this question by looking at the top-level process only (i.e., activities A, B and C); the model allows to hide the content of sub-processes A and C for answering this specific question, hence reducing the number of elements to be considered.

 

Figure 1: Example of hierarchical structuring

 


Split-Attention Effect

So far we have illustrated that abstraction through hierarchical structuring can help to reduce mental effort. However, the introduction of sub-models also has its downsides. When extracting information from the model, the reader has to take into account several sub-models, thereby switching attention between sub-models. The resulting split-attention effect [11] then leads to increased mental effort, nullifying beneficial effects from abstraction. In fact, too many sub-models impede understandability, as pointed out in [2]. Again, as for abstraction, we assume that sub-models are viewed separately. To illustrate this, consider the BPMN model shown in Figure 1. To assess whether activity J can be executed after activity E, the reader has to switch between the top-process as well as sub-processes A and C, causing her attention to split between these models, thus increasing mental effort.

The interplay of abstraction and the split-attention effect is described in detail in:
S. Zugal, J. Pinggera, J. Mendling and H. Reijers: Assessing the Impact of Hierarchy on Model Understandability—A Cognitive Perspective. In: Proc. EESSMod ’11, pp. 18–27, 2011.  

References

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[2] Damij, N.: Business process modelling using diagrammatic and tabular techniques. Business Process Management Journal 13 (2007) 70-90

[3] Davies, R.: Business Process Modelling With Aris: A Practical Guide. Springer (2001)

[4] Sharp, A., McDermott, P.: Workow Modeling: Tools for Process Improvement and Application Development. Artech House (2011)

[5] Kock, N.F.: Product flow, breadth and complexity of business processes: An empirical study of 15 business processes in three organizations. Business Process Re-engineering & Management Journal 2 (1996) 8-22

[6] Mendling, J., Reijers, H.A., van der Aalst, W.M.P.: Seven process modeling guidelines (7pmg). Information & Software Technology 52 (2010) 127-136

[7] Cruz-Lemus, J.A., Genero, M., Manso, M.E., Piattini, M.: Evaluating the E ffectof Composite States on the Understandability of UML Statechart Diagrams. In: Proc. MODELS ’05. (2005) 113-125

[8] Cruz-Lemus, J.A., Genero, M., Manso, M.E., Morasca, S., Piattini, M.: Assessing the understandability of UML statechart diagrams with composite states-A family of empirical studies. Empir Software Eng 25 (2009) 685-719

[9] Burton-Jones, A., Meso, P.N.: Conceptualizing systems for understanding: An empirical test of decomposition principles in object-oriented analysis. ISR 17 (2006) 38-60

[10] Moody, D.L.: Cognitive Load Eff ects on End User Understanding of Conceptual Models: An Experimental Analysis. In: Proc. ADBIS ’04. (2004) 129-143

[11] Sweller, J., Chandler, P.: Why Some Material Is Dicult to Learn. Cognition and Instruction 12 (1994) 185-233