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Software Team Productivity Factor in Constructive Cost Model for Software Development Effort Estimation
Okure U. Obot, Edward Ndarake Udo, Peter G. Obike
Pages - 1 - 21     |    Revised - 30-06-2022     |    Published - 01-08-2022
Volume - 10   Issue - 1    |    Publication Date - August 2022  Table of Contents
Software Development Effort, COCOMO, Team Productivity Factor, Principal Component Analysis, ANFIS, Back Propagation, Hybrid Learning Algorithm.
One of the models used to implement software development effort estimates is the Constructive Cost Model (COCOMO) and the attributes of this model are said to contain some level of imprecision. This study was motivated by the need to accurately estimate software development effort and also reduces the imprecision contained in the COCOMO. A neuro-fuzzy constructive cost model by Kaur et al., (2018) was studied and found to contain some of the desirable features of a neuro-fuzzy approach. It handles imprecision using Adaptive Neuro-Fuzzy Inference System (ANFIS) with a large dimension of datasets and does not consider software team members productivity. This work introduces software team productivity factor into the conventional COCOMO and converts it to COCOMO II using model definition manual and Rosetta Stone and also considers reducing the number of inputs from 23 to 6. With data gathered from PROMISE repository (NASA project), an ANFIS-based model was built. The new model with the productivity factor was implemented along with that of Kaur et al., (2018) in the MATLAB 2021 programming environment. Findings reveal that with 6 out of the 23 attributes of PROMISE datasets, the ANFIS model (Hybrid and Back Propagation) with the productivity factor performs better than the Kaur et al., (2018) model. The implication is that the productivity of the team members working on a software project can add up or reduce the actual person-hours (Effort) required to develop a software. During the experiments, six (6) important COCOMO inputs that software managers should place more emphasis on during the planning stage were identified.
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Dr. Okure U. Obot
Department of Computer Science, Faculty of Science, University of Uyo, Uyo - Nigeria
Dr. Edward Ndarake Udo
Department of Computer Science, Faculty of Science, University of Uyo, Uyo - Nigeria
Mr. Peter G. Obike
Department of Computer Science, College of Physical and Applied Sciences, Michael Okpara University of Agriculture, Umudike - Nigeria

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