Document Type

Article

Source of Publication

Empirical Software Engineering

Publication Date

6-1-2013

Abstract

Recent studies have reported that Support Vector Regression (SVR) has the potential as a technique for software development effort estimation. However, its prediction accuracy is heavily influenced by the setting of parameters that needs to be done when employing it. No general guidelines are available to select these parameters, whose choice also depends on the characteristics of the dataset being used. This motivated the work described in (Corazza et al. 2010), extended herein. In order to automatically select suitable SVR parameters we proposed an approach based on the use of the meta-heuristics Tabu Search (TS). We designed TS to search for the parameters of both the support vector algorithm and of the employed kernel function, namely RBF. We empirically assessed the effectiveness of the approach using different types of datasets (single and cross-company datasets, Web and not Web projects) from the PROMISE repository and from the Tukutuku database. A total of 21 datasets were employed to perform a 10-fold or a leave-one-out cross-validation, depending on the size of the dataset. Several benchmarks were taken into account to assess both the effectiveness of TS to set SVR parameters and the prediction accuracy of the proposed approach with respect to widely used effort estimation techniques. The use of TS allowed us to automatically obtain suitable parameters' choices required to run SVR. Moreover, the combination of TS and SVR significantly outperformed all the other techniques. The proposed approach represents a suitable technique for software development effort estimation. © 2011 Springer Science+Business Media, LLC.

ISSN

1382-3256

Publisher

Springer Science and Business Media LLC

Volume

18

Issue

3

First Page

506

Last Page

546

Disciplines

Computer Sciences

Keywords

Effort estimation, Search based techniques, Support vector regression, Tabu search

Scopus ID

84878701385

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Hybrid: This publication is openly available in a subscription-based journal/series

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