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Guo, X.L., Wang, H.Y. and Glass, D.H. (2012) A Growing Bayesian Self-Organizing Map for Data Clustering. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), 2, 708-713.

has been cited by the following article:

  • TITLE: Software Reusability Classification and Predication Using Self-Organizing Map (SOM)

    AUTHORS: Amjad Hudaib, Ammar Huneiti, Islam Othman

    KEYWORDS: Component Based System Development (CBSD), Software Reusability, Software Metrics, Classification, Self-Organizing Map (SOM)

    JOURNAL NAME: Communications and Network, Vol.8 No.3, August 17, 2016

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