Abstract
Software cost estimation is a process of planning, risk analysis, and decision making for project management in software development. Cost of project development encompasses a software project’s effort and development time. One popular model of software cost estimation is constructive cost model (COCOMO) model, which is a mathematical model proposed by Boehm, used for estimate the software effort and development time. The objective of this paper is to improve the basic COCOMO model’s coefficients for modern programming languages like Python, R, C++, etc. Many techniques were presented in the past for effort and time estimation using machine learning. But all these techniques were trained and tested for older programming languages. In order to improve the accuracy of COCOMO for modern programming languages, six Python projects have been considered and genetic algorithm (GA) is applied in these projects to define new values for basic COCOMO coefficients and the development time is calculated for Python projects. The time estimated using GA coefficients is compared with the original COCOMO and actual time. Using mean magnitude relative error, the error from the original COCOMO time is 54.49% and error from GA time is 21.23%.
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Change history
19 August 2020
In the original version of the book, the following belated correction has been incorporated: The affiliation of Dr. Neha Chaudhary and Amrita Sharma has been changed from “Department of Computer Science and Engineering, Manipal University, Jaipur, India” to “Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, India”. The erratum chapter and the book have been updated with the change.
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Sharma, A., Chaudhary, N. (2020). Software Cost Estimation for Python Projects Using Genetic Algorithm. In: Bansal, J., Gupta, M., Sharma, H., Agarwal, B. (eds) Communication and Intelligent Systems. ICCIS 2019. Lecture Notes in Networks and Systems, vol 120. Springer, Singapore. https://doi.org/10.1007/978-981-15-3325-9_11
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DOI: https://doi.org/10.1007/978-981-15-3325-9_11
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