Open Access
ARTICLE
KAEA: A Novel Three-Stage Ensemble Model for Software Defect Prediction
Nana Zhang1, Kun Zhu1, Shi Ying1, *, Xu Wang2
1 School of Computer Science, Wuhan University, Wuhan, 430072, China.
2 Department of Computer Science, Vrije University Amsterdam, Amsterdam, 1081HV, The Netherlands.
* Corresponding Author: Shi Ying. Email: .
Computers, Materials & Continua 2020, 64(1), 471-499. https://doi.org/10.32604/cmc.2020.010117
Received 11 February 2020; Accepted 02 April 2020; Issue published 20 May 2020
Abstract
Software defect prediction is a research hotspot in the field of software
engineering. However, due to the limitations of current machine learning algorithms, we
can’t achieve good effect for defect prediction by only using machine learning algorithms.
In previous studies, some researchers used extreme learning machine (ELM) to conduct
defect prediction. However, the initial weights and biases of the ELM are determined
randomly, which reduces the prediction performance of ELM. Motivated by the idea of
search based software engineering, we propose a novel software defect prediction model
named KAEA based on kernel principal component analysis (KPCA), adaptive genetic
algorithm, extreme learning machine and Adaboost algorithm, which has three main
advantages: (1) KPCA can extract optimal representative features by leveraging a nonlinear
mapping function; (2) We leverage adaptive genetic algorithm to optimize the initial
weights and biases of ELM, so as to improve the generalization ability and prediction
capacity of ELM; (3) We use the Adaboost algorithm to integrate multiple ELM basic
predictors optimized by adaptive genetic algorithm into a strong predictor, which can
further improve the effect of defect prediction. To effectively evaluate the performance of
KAEA, we use eleven datasets from large open source projects, and compare the KAEA
with four machine learning basic classifiers, ELM and its three variants. The experimental
results show that KAEA is superior to these baseline models in most cases.
Keywords
Cite This Article
N. Zhang, K. Zhu, S. Ying and X. Wang, "Kaea: a novel three-stage ensemble model for software defect prediction,"
Computers, Materials & Continua, vol. 64, no.1, pp. 471–499, 2020.
Citations