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An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation

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journal contribution
posted on 01.10.2019, 14:18 by Mohammad Azzeh, Ali Bou Nassif, Leandro L. Minku
Context Effort adjustment is an essential part of analogy-based effort estimation, used to tune and adapt nearest analogies in order to produce more accurate estimations. Currently, there are plenty of adjustment methods proposed in literature, but there is no consensus on which method produces more accurate estimates and under which settings. Objective This paper investigates the potential of ensemble learning for variants of adjustment methods used in analogy-based effort estimation. The number k of analogies to be used is also investigated. Method We perform a large scale comparison study where many ensembles constructed from n out of 40 possible valid variants of adjustment methods are applied to eight datasets. The performance of each method was evaluated based on standardized accuracy and effect size. Results The results have been subjected to statistical significance testing, and show reasonable significant improvements on the predictive performance where ensemble methods are applied. Conclusion Our conclusions suggest that ensembles of adjustment methods can work well and achieve good performance, even though they are not always superior to single methods. We also recommend constructing ensembles from only linear adjustment methods, as they have shown better performance and were frequently ranked higher.

Funding

Mohammad Azzeh and Ali Bou Nassif are grateful to the Applied Science University, Amman, Jordan, for the financial support granted to carry out this research. Leandro Minku is grateful to EPSRC for the financial support given through the grant no. EP/J017515/1.

History

Citation

Journal of Systems and Software, 2015, 103, pp. 36-52

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Science

Version

AM (Accepted Manuscript)

Published in

Journal of Systems and Software

Publisher

Elsevier

issn

0164-1212

Acceptance date

16/01/2015

Copyright date

2015

Available date

01/10/2019

Publisher version

https://www.sciencedirect.com/science/article/pii/S0164121215000199?via=ihub

Language

en

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