Almomani, A, Gupta, B B, Alauthman, Mohammad, al-Qerem, Ahmad, Alangari, Someah, Ali, Ali Mohd, Nabo, Ahmad, Aldweesh, Amjad and Jebreen, Issam (2023) Machine Learning for Accurate Software Development Cost Estimation in Economically and Technically Limited Environments. International Journal of Software Science and Computational Intelligence, 15 (1). pp. 1-24. ISSN 1942-9045
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Cost estimation for software development is crucial for project planning and management. Several regression models have been developed to predict software development costs, using historical datasets of previous projects. Accurate cost estimation in software development is heavily influenced by the relevance and quality of the cost estimation dataset and its suitability to the software development environment. The currently available cost estimation datasets are limited to North American and European environments, leaving a gap in the representation of other economically and technically constrained software industries. In this article, the authors evaluate the performance of regression models using the SEERA dataset, which highly represents these constrained environments. This study provides insights into selecting regression models for cost estimation in software development. It highlights the importance of using appropriate models based on the specific software development model and dataset used in the estimation process. In the performance evaluations of eight regression models, including elastic net, lasso regression, linear regression, neural network, RANSACRegressor, random forest, ride regression, and SVM, for cost estimation in different software models, along with correlation coefficients and accuracy indicators, were reported. The results showed that SVM and random forest indicated superior performance. However, the elastic net, lasso regression, linear regression, neural network, and RANSACRegressor models also demonstrated exemplary performance in cost estimation.
Affiliation: | Skyline University College |
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SUC Author(s): | Almomani, A ORCID: https://orcid.org/0000-0002-8808-6114 and Gupta, B B |
All Author(s): | Almomani, A, Gupta, B B, Alauthman, Mohammad, al-Qerem, Ahmad, Alangari, Someah, Ali, Ali Mohd, Nabo, Ahmad, Aldweesh, Amjad and Jebreen, Issam |
Item Type: | Article |
Subjects: | B Information Technology > BF Software Emgineeting B Information Technology > BL Machine Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 26 Jan 2024 16:47 |
Last Modified: | 26 Jan 2024 16:47 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/799 |
Publisher URL: | https://doi.org/10.4018/IJSSCI.331753 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/17938 |
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