Estimation of software project effort with genetic algorithm and support vector regression 


Vol. 16,  No. 5, pp. 729-736, Oct.  2009
10.3745/KIPSTD.2009.16.5.729


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  Abstract

The accurate estimation of software development cost is important to a successful development in software engineering. Until recent days, the model using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software cost using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying genetic algorithm. The proposed GA-SVR model outperform some recent results reported in the literature.

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[IEEE Style]

K. T. Kwon and S. K. Park, "Estimation of software project effort with genetic algorithm and support vector regression," The KIPS Transactions:PartD, vol. 16, no. 5, pp. 729-736, 2009. DOI: 10.3745/KIPSTD.2009.16.5.729.

[ACM Style]

Ki Tae Kwon and Soo Kwon Park. 2009. Estimation of software project effort with genetic algorithm and support vector regression. The KIPS Transactions:PartD, 16, 5, (2009), 729-736. DOI: 10.3745/KIPSTD.2009.16.5.729.

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