• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.81-85
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• 2008

The coupling measurements of object oriented programs can be valuable information for various maintenance tasks and there exists a variety of metrics proposed by numerous researchers. Those metrics, however, cannot measure the strength of dependencies between classes, but only imply the existence of dependencies. Hence they are of limited value for assisting maintenance tasks such as refactoring and system decomposition, which requires information about the strength of dependency. In this paper, a coupling metric which can measure the strength of dependence as well as detect the existence of dependencies is proposed. Our coupling metric is evaluated based on the cost required for performing the maintenance tasks. We have applied the proposed coupling metric to an example of system decomposition in an effort to assess the potential benefits of our approach for maintenance tasks.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.651-660
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• 2013

Object oriented design brought up improvement of productivity and software quality by adopting some concepts such as inheritance and encapsulation. However, both the number of software's classes and object couplings are increasing as the software volume is becoming larger. The object coupling between classes is closely related with software complexity, and high complexity causes decreasing software quality. In order to solve the object coupling issue, IT-field researchers adopt a component based development and software quality metrics. The component based development requires explicit representation of dependencies between classes and the software quality metrics evaluates quality of software. As part of the research, we intend to gain a basic data that will be used on decomposing software. We focused on properties of the linkage between classes rather than previous studies evaluated and accumulated the qualities of individual classes. Our method exploits machine learning technique to analyze the properties of linkage and predict the strength of dependency between classes, as a new perspective on analyzing software property.