Research Article
Early Size and Effort Estimation of Mobile Application Development
@ARTICLE{10.4108/eai.31-5-2021.170010, author={Ziema Mushtaq and Sami Alshmrany and Fahad Alturise and Tamim Alkhalifah}, title={Early Size and Effort Estimation of Mobile Application Development}, journal={EAI Endorsed Transactions on Energy Web}, volume={9}, number={37}, publisher={EAI}, journal_a={EW}, year={2021}, month={5}, keywords={Effort Estimation, Mobile application development, Mobile effort estimation methodology, Hybrid Machine learning techniques, CPEEM Approaches}, doi={10.4108/eai.31-5-2021.170010} }
- Ziema Mushtaq
Sami Alshmrany
Fahad Alturise
Tamim Alkhalifah
Year: 2021
Early Size and Effort Estimation of Mobile Application Development
EW
EAI
DOI: 10.4108/eai.31-5-2021.170010
Abstract
With the increased complexity in mobile applications, many challenges and issues emerged for the software project management team to develop mobile application effectively and accurately. Effort estimation is one of the most critical issues the Software management project team faces in general, and the mobile application development team in specific. Effort estimation helps to approximate the cost required for successful software application development. The mobile application is distinct in various aspects from the traditional software and web-based applications. There is a need for a specific methodology to be followed for accurate estimation of size and efforts. This research aims to review the effectiveness of COSMIC and Machine Learning techniques in performing mobile effort estimation and propose a hybrid approach for efficient mobile effort estimation. This research work's mains represent the methodology followed to achieve the input parameters and mobile applications' efforts using a tailor-made approach. The significance of this research work is to propose a framework that will help both researchers and mobile application estimators approximate the efficient efforts precisely.
Copyright © 2021 Ziema Mushtaq et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.