Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2014.050728
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 5 Issue 7, 2014.
Abstract: On time and within budget software project development represents a challenge for software project managers. Software management activities include but are not limited to: estimation of project cost, development of schedules and budgets, meeting user requirements and complying with standards. Recruiting development team members is a sophisticated problem for a software project manager. Since the utmost cost in software development effort is manpower, software project effort and is associated cost estimation models are used in estimating the effort required to complete a project. This effort estimate can then be converted into dollars based on the proper labor rates. An initial development team needs to be selected not only at the beginning of the project but also during the development process. It is important to allocate the necessary team to a project and efficiently distribute their effort during the development life cycle. In this paper, we provide our initial idea of developing a prediction model for defining the estimated required number of test workers of a software project during the software testing process. The developed models utilize the test instance and the number of observed faults as input to the proposed models. Artificial Neural Networks (ANNs) successfully build the dynamic relationships between the inputs and output and produce and accurate predication estimates.
Alaa F. Sheta, Sofian Kassaymeh and David Rine, “Estimating the Number of Test Workers Necessary for a Software Testing Process Using Artificial Neural Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 5(7), 2014. http://dx.doi.org/10.14569/IJACSA.2014.050728