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A Hybrid Model for Improving Software Cost Estimation in Global Software Development

Mehmood Ahmed1,3,*, Noraini B. Ibrahim1, Wasif Nisar2, Adeel Ahmed3, Muhammad Junaid3,*, Emmanuel Soriano Flores4, Divya Anand4

1 Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia Parit Raja, Batu Pahat, 86400, Malaysia
2 Department of Computer Science, COMSATS University Wah Cantt Campus, Islamabad, 47010, Pakistan
3 Department of Information Technology, The University of Haripur, Khyber Pakhtunkhwa, 22620, Pakistan
4 Engineering Research Innovation Group, Universidad Europea del Atlantico, Santander, 39011, Spain

* Corresponding Authors: Mehmood Ahmed. Email: email; Muhammad Junaid. Email: email

Computers, Materials & Continua 2024, 78(1), 1399-1422. https://doi.org/10.32604/cmc.2023.046648

Abstract

Accurate software cost estimation in Global Software Development (GSD) remains challenging due to reliance on historical data and expert judgments. Traditional models, such as the Constructive Cost Model (COCOMO II), rely heavily on historical and accurate data. In addition, expert judgment is required to set many input parameters, which can introduce subjectivity and variability in the estimation process. Consequently, there is a need to improve the current GSD models to mitigate reliance on historical data, subjectivity in expert judgment, inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns. This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks (ANN) to address these challenges. The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts. This article compares the effectiveness of the proposed model with state-of-the-art machine learning-based models for software cost estimation. Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy, outperforming existing state-of-the-art models. The findings indicate the potential of combining COCOMO II, ANN, and additional GSD-based cost drivers to transform cost estimation in GSD.

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Cite This Article

M. Ahmed, N. B. Ibrahim, W. Nisar, A. Ahmed, M. Junaid et al., "A hybrid model for improving software cost estimation in global software development," Computers, Materials & Continua, vol. 78, no.1, pp. 1399–1422, 2024.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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