Due to rapid development in software industry, it was necessary to reduce time and efforts in the\nsoftware development process. Software Reusability is an important measure that can be applied\nto improve software development and software quality. Reusability reduces time, effort, errors,\nand hence the overall cost of the development process. Reusability prediction models are established\nin the early stage of the system development cycle to support an early reusability assessment.\nIn Object-Oriented systems, Reusability of software components (classes) can be obtained\nby investigating its metrics values. Analyzing software metric values can help to avoid developing\ncomponents from scratch. In this paper, we use Chidamber and Kemerer (CK) metrics suite in order\nto identify the reuse level of object-oriented classes. Self-Organizing Map (SOM) was used to\ncluster datasets of CK metrics values that were extracted from three different java-based systems.\nThe goal was to find the relationship between CK metrics values and the reusability level of the\nclass. The reusability level of the class was classified into three main categorizes (High Reusable,\nMedium Reusable and Low Reusable). The clustering was based on metrics threshold values that\nwere used to achieve the experiments. The proposed methodology succeeds in classifying classes\nto their reusability level (High Reusable, Medium Reusable and Low Reusable). The experiments\nshow how SOM can be applied on software CK metrics with different sizes of SOM grids to provide\ndifferent levels of metrics details. The results show that Depth of Inheritance Tree (DIT) and\nNumber of Children (NOC) metrics dominated the clustering process, so these two metrics were\ndiscarded from the experiments to achieve a successful clustering. The most efficient SOM topology\n[2 Ã?â?? 2] grid size is used to predict the reusability of classes.
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