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

  • Authors:

  • A. Corazza;S. Di Martino;F. Ferrucci;C. Gravino;E. Mendes

  • Affiliations:

  • University of Napoli “Federico II”, Via Cinthia, I-80126, Napoli, Italy;University of Napoli “Federico II”, Via Cinthia, I-80126, Napoli, Italy;University of Salerno, Via Ponte Don Melillo, I-84084 Fisciano (SA) Italy;University of Salerno, Via Ponte Don Melillo, I-84084 Fisciano (SA) Italy;The University of Auckland, Private Bag 92019, Auckland, New Zealand

  • Venue:
  • ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
  • Year:
  • 2009

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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.