About understanding and communicating an object model

A model by definition is a simplification of reality. The main reason for model is always a simplification of existing or a design of something to be. Sometimes as an extreme a model can be one-to-one, but calling that a “model” is actually somewhat misleading.

The reason to have a simplification of something in most cases is the fact that the total amount of detail would be unmanageable and not understandable and for this most general reason we use models.

As we model concrete 3D things like houses we can have the model in 3D also, but the size of the model is far smaller than the size or the real thing. The downsizing evidently will disregard or destroy some amount of detail and simplify the model compared with reality . Another way of modelling these things is a mapping done from reality (current or future) to some kind of language. In the case of building this language consists of geometrical figures, which maps to realty. For instance a floor plan consists of together joined rectangles and a set of other graphical signs like door and windows.

To understand meaning of a model implies that the person in question is able to “see” the corresponding reality trough the model. If we use a mapping from reality to not self-evident language (like in case of house plans or maps), then the prerequisite for understanding the model is the knowledge of the discreption language.

Understanding is not a two-value function (on ; off) but a complex multi value thing. Thus it is very risky to say that a person doesn’t or a group do don’t understand the model. Rather we should consider to what extent someone understands the model.

It is obvious that the more complex the model is the more difficult it is to understand all aspects of the model. Even the “understanding” concept is difficult. The complexity of a model depends on two independent sources: 1) the domain in concern intrinsic complexity and 2) the chosen abstraction level of the model.

The complexity of the domain cannot directly be influenced. Indirectly this can be influenced by limiting size of reality to be modelled. The level of abstraction is completely in hands of the modellers. The semantic descriptiveness of a class and its level of abstraction has the following dependency with ease other:

This means that when chosen the level of abstraction of a class this with determine the semantic content or richness of the concept. So the lower the level of abstraction is the more semantic meaning the class name (&definition) will have. Of course it is good to have as much semantics as possible but the contradicting demand is to reduce the amount of less important details to promote understanding.

To balance the model between these demands the most effective choice is somewhere in between the extremes (the orange line in the figure). A very important aspect of OO-model creation is that towards the end of modelling a given domain the emphasis is on simplifying the model as much as possible without losing its semantic weight or value. This means that we approach a point where leaving something away will have a dramatic effect on the model collapsing it totally. Here the model has reached its complexity minim (at a given semantic level). If someone then demands to reduce the complexity of the model being too complex to understand this will mean no model at all!

The abstract object model consists of a class model and a set of collaboration diagrams. Class model contain all the classes ( concepts) found during the domain analysis and all the relationships between the classes. This class model can be represented in one or several class diagrams. Even if the model is represented in more than one class diagram the model itself is always one and connected. It is moderately ease to show that this model if it is correctly done is in the complexity minim of all models.

I will continue with understanding the model. First we have several different methods to model reality. Some of them are structurally simpler than OO-models like for instance use cases which have hardly any structure at all. Then we can move the whole model (ie. all classes) up and down the semantic-abstraction triangle. If we chose very high abstraction level we loosed a lot of detail and we will have a simple model. So simple models are far easier to understand than the models that have more structure, semantics and more fine grain behaviour defined.

The best OO-model in the middle of the triangle is most effective model. Thus it leads to most effective implementation as an application.

The model include also a set of collaboration diagrams describing a few cases with the deepest and most important sequences, which describes some instances of  creation of the most important event object of the business cycle. These diagrams consists of objects not classes. For this reason the are very concrete and far easier to understand. Sometime we can draw additional static object diagrams to explain complex structure without any messaging.

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