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Ensembling artificial bee colony with analogy-based estimation to improve software development effort prediction

Shah, M. A. and Abang Jawawi, D. N. and Isa, M. A. and Younas, M. and Abdelmaboud, A. and Sholichin, F. (2020) Ensembling artificial bee colony with analogy-based estimation to improve software development effort prediction. IEEE Access, 8 . pp. 58402-58415. ISSN 21693536

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Official URL: http://dx.doi.org/10.1109/ACCESS.2020.2980236

Abstract

Analogy-Based Estimation (ABE) is one of the promising estimation models used for predicting the software development effort. Researchers proposed different variants of the ABE model, but still, the most suitable procedure could not be produced for accurate estimation. In this study, an artificial Bee colony guided Analogy-Based Estimation (BABE) model is proposed which ensembles Artificial Bee Colony (ABC) with ABE for accurate estimation. ABC produces different weights, out of which the most appropriate is infused in the similarity function of ABE during the stage of model training, which are later used in the testing stage for evaluation. There are six real datasets utilized for simulating the model procedure. Five of these datasets are taken from the PROMISE repository. The predictive performance is improved for BABE over the existing ones. The most significant of its performance is found on the International Software Benchmarking Standards Group (ISBSG) dataset.

Item Type:Article
Uncontrolled Keywords:analogy based estimation, artificial bee colony, cost estimation
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:93103
Deposited By: Narimah Nawil
Deposited On:07 Nov 2021 05:54
Last Modified:07 Nov 2021 05:54

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