Abdelali, Z. and Hicham, M. and Abdelwahed, N. (2019) An ensemble of optimal trees for software development effort estimation. Lecture Notes in Networks and Systems, 66. pp. 55-68.

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Abstract

Accurate estimation of software development effort plays a pivotal role in managing and controlling the software development projects more efficiently and effectively. Several software development effort estimation (SDEE) models have been proposed in the literature including machine learning techniques. However, none of these models proved to be powerful in all situation and their performance varies from one dataset to another. To overcome the weaknesses of single estimation techniques, the ensemble methods have been recently employed and evaluated in SDEE. In this paper, we have developed an ensemble of optimal trees for software development effort estimation. We have conducted an empirical study to evaluate and compare the performance of this optimal trees ensemble using five popular datasets and the 30 hold-out validation method. The results show that the proposed ensemble outperforms regression trees and random forest models in terms of MMRE, MdMRE and Pred(0.25) in all datasets used in this paper. © Springer Nature Switzerland AG 2019.

Item Type: Article
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:45
URI: http://eprints.umi.ac.ma/id/eprint/2281

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