ew 18: e39

Research Article

Early Size and Effort Estimation of Mobile Application Development

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  • @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: Online First},
        volume={},
        number={},
        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
Ziema Mushtaq1,*, Sami Alshmrany2, Fahad Alturise3, Tamim Alkhalifah3
  • 1: Research Scholar, Maulana Azad National Urdu University, Hyderabad, India
  • 2: Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia
  • 3: Computer Department, College of Science and Arts in ArRass Qassim University, ArRass, Qassim, Saudi Arabia
*Contact email: Sheikhz123@gmail.com

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.