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Title
Abstract
FAQs
Figures
Motivation and Some Background
Discussion and Fundamental Issues
References

Basic Concepts for Creating Visual Models

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Abstract
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AI

The paper discusses the significance of visual modeling in software development and system design, emphasizing its benefits such as enhancing understanding, simplifying representation, and facilitating communication with stakeholders. It critiques the existing modeling notations, particularly ULM and Fundamental Modeling Concepts (FMCs), for their complexity and lack of attention to artistic and expressive aspects of visual design. The paper advocates for educating IT professionals on key modeling principles, highlighting the importance of effective visual representation in creating robust computer systems.

FAQs

sparkles

AI

What explains the neglect of modeling in software development?add

The research identifies several reasons for modeling neglect, such as lack of uniform foundations and insufficient training in requirements engineering. Furthermore, time constraints and complexity contribute to a preference for quicker solutions over thorough modeling.

How do aesthetic principles influence visual modeling effectiveness?add

Aesthetic principles play a crucial role in the effectiveness of visual models, impacting clarity and user comprehension. The study emphasizes that attractive layouts and harmonized designs lead to more intuitive and usable representations.

What challenges are associated with selecting appropriate modeling notations?add

These challenges stem from the diversity of notations and lack of training for stakeholders, complicating optimal selection. The findings suggest that unfamiliarity leads to suboptimal choices based on trial-and-error in practice.

When is simplification necessary in modeling practices?add

Simplification is crucial when models become overly complex, hindering understanding and usability. The paper underscores that a balance must be maintained between detail and comprehensibility to serve the modeling purpose effectively.

What indicates the importance of abstraction in modeling systems?add

Abstraction allows for describing conceptual architectures at various levels, enhancing the model's clarity and purpose. The research indicates that precise abstraction must consider both system functionality and usability.

Figures (5)
Figure: 1 Three Key Attributes of System Models [1] Good models should promote reuse. They should be restrictive, descriptive, suggestive and self-explanatory. For comprehension it should contain a sufficient level of detail. Tidiness implies that it is neatly laid out and drawn up using appropriate scales. The model must be suitable for visualization. Principles of layout harmonization and proper aesthetics in its design must be self-evident. Artistic principles should also be included if possible. The topic of modeling is still evolving and new methodsand techniques are constantly being created as the need aricac II. PROBLEM STATEMENTS Several reasons exist to explain why modeling is neglected: i) there is a lack of uniform modeling foundation, ii) so many diverse notations exist. Which should be chosen? iii) there is a lack of training in requirements engineering and modeling, iv) it is time consuming to apply notations. When should we stop applying them? How much is enough? Which should be selected? v) there is a lack of education and training about the importance of models. For more details refer to [1]-[22]. Normally a user gets an informal education in modeling. The user learns to use models indirectly in his education and work experience. The skill of modeling is not taught. The problem is that normally no systematic approach to modeling has been presented and the importance of modeling as a topic in its own right is ignored. Then with work experience the user stands a better chance of selecting the right model for the job and appreciate the use of robust modeling.
Figure: 1 Three Key Attributes of System Models [1] Good models should promote reuse. They should be restrictive, descriptive, suggestive and self-explanatory. For comprehension it should contain a sufficient level of detail. Tidiness implies that it is neatly laid out and drawn up using appropriate scales. The model must be suitable for visualization. Principles of layout harmonization and proper aesthetics in its design must be self-evident. Artistic principles should also be included if possible. The topic of modeling is still evolving and new methodsand techniques are constantly being created as the need aricac II. PROBLEM STATEMENTS Several reasons exist to explain why modeling is neglected: i) there is a lack of uniform modeling foundation, ii) so many diverse notations exist. Which should be chosen? iii) there is a lack of training in requirements engineering and modeling, iv) it is time consuming to apply notations. When should we stop applying them? How much is enough? Which should be selected? v) there is a lack of education and training about the importance of models. For more details refer to [1]-[22]. Normally a user gets an informal education in modeling. The user learns to use models indirectly in his education and work experience. The skill of modeling is not taught. The problem is that normally no systematic approach to modeling has been presented and the importance of modeling as a topic in its own right is ignored. Then with work experience the user stands a better chance of selecting the right model for the job and appreciate the use of robust modeling.
Basic Concepts for Creating Visual Models Figure: 2 Dynamic Vs Static Composition Model Attribute Classification [1]
Basic Concepts for Creating Visual Models Figure: 2 Dynamic Vs Static Composition Model Attribute Classification [1]
Visual models created to represent information systems can be developed to create new forms of representation and understanding. The visual models can be combined with pictorial representation, other notations and mathematical expressions as required. The idea of reuse in a model driven engineering approach is presented in [25]. This work gives an indication of the complexity of modeling that remains unsolved and indicates that simplification is of great importance to creating usable models. The ever increasing diversity and heterogeneity of systems and their components complicates things. rT 1... aaxeencocers:: mt Eee: teenie commotess creaswsinc Umtecaniinmm chanac ana ocala: -memxsidowo: ommen Laci «mf tho
Visual models created to represent information systems can be developed to create new forms of representation and understanding. The visual models can be combined with pictorial representation, other notations and mathematical expressions as required. The idea of reuse in a model driven engineering approach is presented in [25]. This work gives an indication of the complexity of modeling that remains unsolved and indicates that simplification is of great importance to creating usable models. The ever increasing diversity and heterogeneity of systems and their components complicates things. rT 1... aaxeencocers:: mt Eee: teenie commotess creaswsinc Umtecaniinmm chanac ana ocala: -memxsidowo: ommen Laci «mf tho
CONCLUDING REMARKS omitting important facts and details. A model can be made i) more complex, ii) simpler. A complex model is more faithful to reality whilst a simpler model is easier to handle. It is a dilemma whether a simpler model is to be preferred over a more detailed one ot vice-versa. Reality shows that it is not possible to create a perfect model. This is because a model is always a restricted representation of a system. To implement more details entails a higher price and more effort that is not always requested. If the model is too large and detailed the model is like a large map which sometimes is useful and at other times might be too much. The larger the model the greater the drawbacks for creating and using it. This problem can be called over-modelling or better explained as overrepresentation where too much detail is given for its purpose. This problem can happen also in software designs where the application is overdesigned or over-engineered where a disproportionate effort is placed on the qualities of the application which are deemed unnecessary. It could also be that many features in the application are never used or really needed. This problem is inherent in many web applications, mobile phones etc. that are developed, where at least 10%-20% of the features might never be used. It seems that the problems of over-engineering and overrepresentation are related to one another. Excessive detail can imply that the system is too difficult to fix and comprehend. Even usability of the system will be problematic with over-engineering. When creating a model at what level of detail should we stop refining the model? This process could be ar ongoing process with no end in sight. A good model should be able to teach by example. The key solution to these problems is simplification in the design and in the constructionstages. Simplification is separate topic that is based on principles of reduction and composition. Simplification of models used in computing is non-trivial and deserves a lot of attention. Simplification implies straightforward communication we a &Ss m=) | 63 gene: 7: * * This work deals with basic key principles for creating good system and software models. But it is far off from finding a solution to this problematic area. Requirements engineering and software engineering have several problems that have withstood the test of time and that seem to remain unsolvable. The more complex and fragmented systems become, the greater is the significance and importance of sound modeling principles. A one size fits all solution does not work. Formal methods and formal notations could be combined with the visual diagrams for creating more robust models. Simplification is a universal principle that needs to be considered when modeling systems. Even though ap x 1 1 a1 ?. ary 1 Z a: + -AT
CONCLUDING REMARKS omitting important facts and details. A model can be made i) more complex, ii) simpler. A complex model is more faithful to reality whilst a simpler model is easier to handle. It is a dilemma whether a simpler model is to be preferred over a more detailed one ot vice-versa. Reality shows that it is not possible to create a perfect model. This is because a model is always a restricted representation of a system. To implement more details entails a higher price and more effort that is not always requested. If the model is too large and detailed the model is like a large map which sometimes is useful and at other times might be too much. The larger the model the greater the drawbacks for creating and using it. This problem can be called over-modelling or better explained as overrepresentation where too much detail is given for its purpose. This problem can happen also in software designs where the application is overdesigned or over-engineered where a disproportionate effort is placed on the qualities of the application which are deemed unnecessary. It could also be that many features in the application are never used or really needed. This problem is inherent in many web applications, mobile phones etc. that are developed, where at least 10%-20% of the features might never be used. It seems that the problems of over-engineering and overrepresentation are related to one another. Excessive detail can imply that the system is too difficult to fix and comprehend. Even usability of the system will be problematic with over-engineering. When creating a model at what level of detail should we stop refining the model? This process could be ar ongoing process with no end in sight. A good model should be able to teach by example. The key solution to these problems is simplification in the design and in the constructionstages. Simplification is separate topic that is based on principles of reduction and composition. Simplification of models used in computing is non-trivial and deserves a lot of attention. Simplification implies straightforward communication we a &Ss m=) | 63 gene: 7: * * This work deals with basic key principles for creating good system and software models. But it is far off from finding a solution to this problematic area. Requirements engineering and software engineering have several problems that have withstood the test of time and that seem to remain unsolvable. The more complex and fragmented systems become, the greater is the significance and importance of sound modeling principles. A one size fits all solution does not work. Formal methods and formal notations could be combined with the visual diagrams for creating more robust models. Simplification is a universal principle that needs to be considered when modeling systems. Even though ap x 1 1 a1 ?. ary 1 Z a: + -AT

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