How effective is Tabu search to configure support vector regression for effort estimation?

Background. Recent studies have shown that Support Vector Regression (SVR) has an interesting potential in the field of effort estimation. However applying SVR requires to carefully set some parameters that heavily affect the prediction accuracy. No general guidelines are available to select these parameters, whose choice also depends on the characteristics of the data set used. This motivates the work described in this paper. Aims. We have investigated the use of an optimization technique in combination with SVR to select a suitable subset of parameters to be used for effort estimation. This technique is named Tabu Search (TS), which is a meta-heuristic approach used to address several optimization problems. Method. We employed SVR with linear and RBF kernels, and used variables' preprocessing strategies (i.e., logarithmic). As for the data set, we employed the Tukutuku cross-company database, which is widely adopted in Web effort estimation studies, and performed a hold-out val...

• Type: Journal
• Year: 2010
• Where: PROMISE
• Authors: Anna Corazza, Sergio Di Martino, Filomena Ferrucci, Carmine Gravino, Federica Sarro, Emilia Mendes
• Keywords: Estimation, Parameters, Prediction Accuracy, PROMISE 2010, Software Engineering