Process modeling
From an operational point of view, the meta-process modeling is aimed at providing guidance for method engineers and application developers.
With advances in technology from larger platform vendors, the vision of business process models (BPM) becoming fully executable (and capable of round-trip engineering) is coming closer to reality every day.
The relationships of a business processes in the context of the rest of the enterprise systems, data, organizational structure, strategies, etc.
Granularity refers to the level of detail of a process model and affects the kind of guidance, explanation and trace that can be provided.
In contrast, software engineers, users, testers, analysts, or software system architects will prefer a fine-grained process model where the details of the model can provide them with instructions and important execution dependencies such as the dependencies between people.
There is also a systematic approach for quality measurement of modeling techniques known as complexity metrics suggested by Rossi et al. (1996).
[11] Authors (Cardoso, Mendling, Neuman and Reijers, 2006) used complexity metrics to measure the simplicity and understandability of a design.
This in turn can lead to a lower understandability, higher maintenance cost and perhaps inefficient execution of the process in question.
Quality issues of process models cannot be evaluated exhaustively however there are four main guidelines and frameworks in practice for such.
Also the broader approach is to be based on semiotics rather than linguistic as was done by Krogstie using the top-down quality framework known as SEQUAL.
Dimensions of Conceptual Quality framework[20] Modeling Domain is the set of all statements that are relevant and correct for describing a problem domain, Language Extension is the set of all statements that are possible given the grammar and vocabulary of the modeling languages used.
Finally, Participant Knowledge is the set of statements that human actors, who are involved in the modeling process, believe should be made to represent the problem domain.
Further work by Krogstie et al. (2006) to revise SEQUAL framework to be more appropriate for active process models by redefining physical quality with a more narrow interpretation than previous research.
Comprehensibility relates to graphical arrangement of the information objects and, therefore, supports the understand ability of a model.
Since the purpose of organizations in most cases is the maximization of profit, the principle defines the borderline for the modeling process.
Correctness, relevance and economic efficiency are prerequisites in the quality of models and must be fulfilled while the remaining guidelines are optional but necessary.
Most experiments carried out relate to the relationship between metrics and quality aspects and these works have been done individually by different authors: Canfora et al. study the connection mainly between count metrics (for example, the number of tasks or splits -and maintainability of software process models);[22] Cardoso validates the correlation between control flow complexity and perceived complexity; and Mendling et al. use metrics to predict control flow errors such as deadlocks in process models.
Most of the guidelines are not easily put to practice but "label activities verb–noun" rule has been suggested by other practitioners before and analyzed empirically.
[26] value of process models is not only dependent on the choice of graphical constructs but also on their annotation with textual labels which need to be analyzed.
The second limitation relates to the prioritizing guideline the derived ranking has a small empirical basis as it relies on the involvement of 21 process modelers only.
This could be seen on the one hand as a need for a wider involvement of process modelers' experience, but it also raises the question, what alternative approaches may be available to arrive at a prioritizing guideline?
