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International Conference on Advanced Information Technology, Services and Systems

AIT2S 2018: Smart Data and Computational Intelligence pp 55–68Cite as

An Ensemble of Optimal Trees for Software Development Effort Estimation

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 66))

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.

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Author information

Authors and Affiliations

  1. ENSAM, 150 Boulevard Nile, Casablanca, Morocco

    Zakrani Abdelali & Moutachaouik Hicham

  2. Faculté des Sciences Ben M’sik, Bd Driss El Harti, Casablanca, Morocco

    Namir Abdelwahed

Authors
  1. Zakrani Abdelali
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  2. Moutachaouik Hicham
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  3. Namir Abdelwahed
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Corresponding author

Correspondence to Zakrani Abdelali .

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Editors and Affiliations

  1. Faculty of Sciences and Technologies, Mohammedia, Morocco

    Faddoul Khoukhi

  2. Faculty of Sciences and Technologies, Settat, Morocco

    Mohamed Bahaj

  3. Faculty of Sciences and Technologies, Boukhalef Tangier, Morocco

    Mostafa Ezziyyani

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Abdelali, Z., Hicham, M., Abdelwahed, N. (2019). An Ensemble of Optimal Trees for Software Development Effort Estimation. In: Khoukhi, F., Bahaj, M., Ezziyyani, M. (eds) Smart Data and Computational Intelligence. AIT2S 2018. Lecture Notes in Networks and Systems, vol 66. Springer, Cham. https://doi.org/10.1007/978-3-030-11914-0_6

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