Models for Efficient Utilization of Resources for Upgrading Android Mobile Technology

Abha Jain, Ankita Bansal

Source Title: International Journal of System Dynamics Applications (IJSDA)11(2)

ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781799898245|DOI: 10.4018/IJSDA.20220701.oa2

MLA

Jain, Abha, and Ankita Bansal. "Models for Efficient Utilization of Resources for Upgrading Android Mobile Technology." IJSDA vol.11, no.2 2022: pp.1-22. http://doi.org/10.4018/IJSDA.20220701.oa2

APA

Jain, A. & Bansal, A. (2022). Models for Efficient Utilization of Resources for Upgrading Android Mobile Technology. International Journal of System Dynamics Applications (IJSDA), 11(2), 1-22. http://doi.org/10.4018/IJSDA.20220701.oa2

Chicago

Jain, Abha, and Ankita Bansal. "Models for Efficient Utilization of Resources for Upgrading Android Mobile Technology," International Journal of System Dynamics Applications (IJSDA) 11, no.2: 1-22. http://doi.org/10.4018/IJSDA.20220701.oa2

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Abstract

The need of the customers to be connected to the network at all times has led to the evolution of mobile technology. Operating systems play a vitol role when we talk of technology. Nowadays, Android is one of the popularly used operating system in mobile phones. Authors have analysed three stable versions of Android, 6.0, 7.0 and 8.0. Incorporating a change in the version after it is released requires a lot of rework and thus huge amount of costs are incurred. In this paper, the aim is to reduce this rework by identifying certain parts of a version during early phase of development which need careful attention. Machine learning prediction models are developed to identify the parts which are more prone to changes. The accuracy of such models should be high as the developers heavily rely on them. The high dimensionality of the dataset may hamper the accuracy of the models. Thus, the authors explore four dimensionality reduction techniques, which are unexplored in the field of network and communication. The results concluded that the accuracy improves after reducing the features.