NDLI: Applying support vector regression for web effort estimation using a cross-company dataset

Content Provider	IEEE Xplore Digital Library
Author	Corazza, A. Di Martino, S. Ferrucci, F. Gravino, C. Mendes, E.
Copyright Year	2009
Description	Author affiliation: The University of Auckland, Private Bag 92019, Auckland, New Zealand (Mendes, E.) \|\| University of Napoli ¿Federico II¿, Via Cinthia, I-80126, Napoli, Italy (Corazza, A.; Di Martino, S.) \|\| University of Salerno, Via Ponte Don Melillo, I-84084 Fisciano (SA) Italy (Ferrucci, F.; Gravino, C.)
Abstract	Support Vector Regression (SVR) is a new generation of Machine Learning algorithms, suitable for predictive data modeling problems. The objective of this paper is to investigate the effectiveness of SVR for Web effort estimation, in particular when dealing with a cross-company dataset. To gain a deeper insight on the method, we carried out an empirical study using four kernels for SVR, namely linear, polynomial, Gaussian, and sigmoid. Moreover, we used two variables' preprocessing strategies (normalization and logarithmic), and two different dependent variables (effort and inverse effort). As a result, SVR was applied using six different configurations for each kernel. As for the dataset, we employed the Tukutuku database, which is widely adopted in Web effort estimation studies. A hold-out approach was adopted to evaluate the prediction accuracy for all the configurations, using two training sets, each containing data on 130 projects randomly selected, and two test sets, each containing the remaining 65 projects. As benchmark, SVR-based predictions were also compared to predictions obtained using Manual StepWise Regression, Case-Based Reasoning, and Bayesian Networks. Our results suggest that SVR performed well, since on the first hold-out, the linear kernel with a logarithmic transformation of variables provided significantly superior prediction accuracy than all the other techniques, while for the second hold-out, the Gaussian kernel achieved significantly superior predictions than all other techniques, except for Manual StepWise Regression.
Starting Page	191
Ending Page	202
File Size	1260981
Page Count	12
File Format	PDF
ISBN	9781424448425
ISSN	19386451
DOI	10.1109/ESEM.2009.5315991
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Machine learning algorithms Databases Bayesian methods Data preprocessing Manuals Predictive models Benchmark testing Polynomials Kernel
Content Type	Text
Resource Type	Article

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