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

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.

Mobile self-organising network positioning algorithm based on node clustering

“…Therefore, the number of clusters under this scenario is set to 2. Finally, through the measured data, the clustering method was compared with the curve fitting method proposed by Raj et al (2016), the linearisation method proposed by Hudaib et al (2016), and the joint solution method proposed by Liu et al (2013b). The cumulative distribution of the average positioning error for the four algorithms is shown in Figure 3.…”

Mobile self-organising network positioning algorithm based on node clustering

“…It can also be applied in the positioning field with high precision requirements. This was an effective way to solve the above problems (Hudaib et al, 2016;Liu et al, 2013a). Zeng et al (2017) used ultra-wideband UWB technology for mobile self-organising network positioning, but the actual positioning process needed to synchronise the time between nodes and obtained a higher range value.…”

A Novel Optimization towards Higher Reliability in Predictive Modelling towards Code Reusability

“…However, we only discuss the related work in this perspective. Hudaib et al [10] have presented a classification-based approach for software reusability using self-organizing map. Khoshkbarforoushha et al [11] have discussed about software metric in order to incorporate reuable components in programming language.…”