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Machine Learning for Accurate Software Development Cost Estimation in Economically and Technically Limited Environments
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Author(s): Mohammad Alauthman (Department of Information Security, Faculty of Information Technology, University of Petra, Amman, Jordan), Ahmad al-Qerem (Computer Science Department, Faculty of Information Technology, Zarqa University, Jordan), Someah Alangari (Department of Computer Science, College of Science and Humanities, Shaqra University, Saudi Arabia), Ali Mohd Ali (Communications and Computer Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Jordan), Ahmad Nabo (Software Engineering Department, Faculty of Information Technology, Zarqa University, Jordan), Amjad Aldweesh (College of Computing and IT, Shaqra University, Saudi Arabia), Issam Jebreen (Computer Science Department, Faculty of Information Technology, Zarqa University, Jordan), Ammar Almomani (Skyline University College, UAE)and Brij B. Gupta (CCRI, Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan & Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India & School of Computing, Skyline University, UAE, & Lebanese American University, Beirut, Lebanon & Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India)
Copyright: 2023
Volume: 15
Issue: 1
Pages: 24
Source title:
International Journal of Software Science and Computational Intelligence (IJSSCI)
Editor(s)-in-Chief: Brij Gupta (Asia University, Taichung City, Taiwan)and Andrew W.H. Ip (University of Saskatchewan, Canada)
DOI: 10.4018/IJSSCI.331753
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Abstract
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.
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