Conceptual Modeling Method for Separation of Concerns and Integration of Structure and Behavior

Conceptual Modeling Method for Separation of Concerns and Integration of Structure and Behavior

Remigijus Gustas (Karlstad University, Sweden) and Prima Gustiené (Karlstad University, Sweden)
Copyright: © 2012 |Pages: 30
DOI: 10.4018/jismd.2012010103
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Identification of discontinuities, separation of concerns, and dealing with the evolutionary changes of requirements is difficult in conceptual modeling. The limited human mind allows focusing on one particular requirement at a time in isolation. One fundamental problem is that all conventional conceptual modeling techniques deal with collections of loosely linked meta-models, which are defined by different types of diagrams. Typically, system development methods project interactive, behavioral, and structural aspects of information systems’ conceptual representations into disparate views. Therefore, the semantic integrity of various architecture dimensions is difficult to achieve. The difficulties stem from the paradigmatic mismatch between static and dynamic constructs. The advantage of the conceptual modeling approach presented in this paper is flexibility. It is demonstrated by case study examples that sequential, underlying, enclosing, overriding, and overlaying interaction loops between actors provide the foundation for the composition of complex scenarios, which span across organizational and technical system boundaries. The presented semantic integration and system decomposition principles target business process modeling experts and information system designers, because they are essential for introducing evolutionary changes and managing complexity of information system conceptualizations.
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Conventional information system (IS) analysis and design methods are restricted in their ability to distinguish among crosscutting concerns, which span across various types of diagrams. It does not matter whether designers apply structured analysis and design (SAD) methods (Gane & Sarson, 1979; Yourdon & Constantine, 1979), object-oriented (Blaha & Rumbaugh, 2005) or component based methods: their expressive power is limited in separating various concerns. Poor concern separation technique is one of the reasons why the way systems are currently built is rather primitive. Consequently, managing the complexity of specifications in software engineering is a problem that can be certainly attributed to various limitations of traditional IS modeling and design methods. The following software engineering issues have remained problematic over the last three decades (Jones, 2009):

  • There are more defects in requirements and design than in source code,

  • Initial requirements are seldom more than 50% complete,

  • There are more defects in test cases than in software itself,

  • About 7% of all defect repairs will accidentally inject new defects,

  • About 5% of software outsource contracts end up in litigation.

In traditional areas of engineering, developers are able to present their design decisions by using a finalized computation-neutral representation. This is not the case in the area of system engineering. The limitations of conventional system modeling methods result in two side effects, which in aspect-oriented software development (Jacobson & Ng, 2005) are known as tangling and scattering. Tangling occurs when the software component or class, instead of fulfilling a particular concern, encapsulates a diverse set of concerns. If a particular concern is spread across multiple components, then this situation is called scattering. When the requirements implied by that concern are modified, the designer must identify all related components and find out how these components are affected by introduced changes. Especially, modifying requirements, which are related to a big number of diagrams, becomes quite problematic. Poor understanding of concerns makes it difficult to make even simple evolutionary extensions of IS specifications. Separation of crosscutting concerns is the first fundamental problem, which cannot be solved without modifying the modeling foundation in system analysis and design. In this paper, we will introduce system decomposition principles, which suggest a new and more natural way of managing complexity in system engineering.

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