Software reusability metrics prediction by using evolutionary algorithms: The interactive mobile learning application RozGaar

Abstract

Considering object oriented program based software metrics (cohesion, coupling and complexity) and their significance to characterize software quality, particularly software component reusability, we have considered six important CK matrices. The predominant reason behind using the measurement technique is the individual relationship with the design aspect and fault-proneness or aging-proneness. The key objective of this paper is to generate employment opening to thousands of people who have different skillsets and furthermore to provide hassle-free services by RozGaar service providers to customers with the help of machine learning techniques. In the current century’s rapid growth of modernization and automation, manual labor is reduced which gives rise to unemployment at mass. If we need technicians, workers, plumbers or drivers who work on daily wages, it is quite difficult to find one in our locality without having any contact references and knowing the quality of the work they provide. This paper helps in filling the gap between the various customers and the service providers. We aim to introduce this paper as an ocean of opportunities for all where people can get jobs on a daily basis and can earn money for their skills. The used application is a dual-platform application that runs on Android devices and on Internet as a website, promising you to provide unmatched services of daily work. To achieve the goal, we used the novel software prediction model, evolutionary algorithms such as decision tree, Rough Set, and Logistic Regression algorithms, to predict software reusability.

Keywords

Software reusability metrics reusability prediction software metrics through regular expression software reused code from apps architecture and framework reused

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