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|pages= 432–437
|url= http://morganasianipar.com/publication/physiological-concept-visible-modeling-feasible-design.html
|doi= 10.4028/www.scientific.net/AMM.493.432|s2cid= 109776405
}}</ref> * Decision-oriented: set of related decisions conducted for the specific purpose of product definition.
* Context-oriented: sequence of contexts causing successive product transformations under the influence of a decision taken in a context.
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Earliest process models reflected the dynamics of the process with a practical process obtained by instantiation in terms of relevant concepts, available technologies, specific implementation environments, process constraints and so on.<ref>Proceedings of the 9th international conference on Software Engineering</ref>
Enormous number of research has been done on quality of models but less focus has been shifted towards the quality of process models. Quality issues of process models cannot be evaluated exhaustively however there are four main guidelines and frameworks in practice for such. These are: top-down quality frameworks, bottom-up metrics related to quality aspects, empirical surveys related to modeling techniques, and pragmatic guidelines.<ref>{{cite journal |
Hommes quoted Wang ''et al.'' (1994)<ref name=ReferenceA/> that all the main characteristic of quality of models can all be grouped under 2 groups namely correctness and usefulness of a model, correctness ranges from the model correspondence to the phenomenon that is modeled to its correspondence to syntactical rules of the modeling and also it is independent of the purpose to which the model is used.
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A common starting point for defining the quality of conceptual model is to look at the linguistic properties of the modeling language of which syntax and semantics are most often applied.
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.<ref name=krogstie/><ref>{{cite journal |
The framework does not however provide ways to determine various degrees of quality but has been used extensively for business process modeling in empirical tests carried out <ref>D. Moody, G. Sindre, T. Brasethvik and A. Sølvberg, Evaluating the quality of process models: empirical testing of a quality framework. In: S. Spaccapietra, S.T. March and Y. Kambayashi, Editors, Conceptual Modeling – ER 2002, 21st International Conference on Conceptual Modeling, Tampere, Finland, October 7–11, 2002, Proceedings, Lecture Notes in Computer Science vol. 2503, Springer (2002), pp. 380–396.</ref>
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The need for a more dynamic view in the semiotic quality framework is particularly evident when considering process models, which themselves often prescribe or even enact actions in the problem ___domain, hence a change to the model may also change the problem ___domain directly. This paper discusses the quality framework in relation to active process models and suggests a revised framework based on this.
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.<ref name =krogstie>{{cite journal |
The other framework in use is Guidelines of Modeling (GoM) <ref>J. Becker, M. Rosemann and C. Uthmann, Guidelines of business process modeling. In: W. van der Aalst, J. Desel and A. Oberweis, Editors, Business Process Management. Models, Techniques, and Empirical Studies, Springer, Berlin (2000), pp. 30–49</ref> based on general accounting principles include the six principles: Correctness, Clarity deals with the comprehensibility and explicitness (System description) of model systems.
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The use of bottom-up metrics related to quality aspects of process models is trying to bridge the gap of use of the other two frameworks by non-experts in modeling but it is mostly theoretical and no empirical tests have been carried out to support their use.
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);<ref>{{cite journal |
The results reveal that an increase in size of a model appears to reduce its quality and comprehensibility.
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