Open Access
ARTICLE
Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization
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, 65(1), 279-308. https://doi.org/10.32604/cmc.2020.011001
Received 13 April 2020; Accepted 28 April 2020; Issue published 23 July 2020
Abstract
Software defect prediction plays an important role in software quality assurance.
However, the performance of the prediction model is susceptible to the irrelevant and
redundant features. In addition, previous studies mostly regard software defect prediction
as a single objective optimization problem, and multi-objective software defect prediction
has not been thoroughly investigated. For the above two reasons, we propose the following
solutions in this paper: (1) we leverage an advanced deep neural network—Stacked
Contractive AutoEncoder (SCAE) to extract the robust deep semantic features from the
original defect features, which has stronger discrimination capacity for different classes
(defective or non-defective). (2) we propose a novel multi-objective defect prediction
model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize
the advanced neural network—Extreme learning machine (ELM) based on state-of-the-art
Pareto optimal solutions according to the features extracted by SCAE. We mainly consider
two objectives. One objective is to maximize the performance of ELM, which refers to the
benefit of the SMONGE model. Another objective is to minimize the output weight norm
of ELM, which is related to the cost of the SMONGE model. We compare the SCAE with
six state-of-the-art feature extraction methods and compare the SMONGE model with
multiple baseline models that contain four classic defect predictors and the MONGE model
without SCAE across 20 open source software projects. The experimental results verify
that the superiority of SCAE and SMONGE on seven evaluation metrics.
Keywords
Cite This Article
N. Zhang, K. Zhu, S. Ying and X. Wang, "Software defect prediction based on stacked contractive autoencoder and multi-objective optimization,"
Computers, Materials & Continua, vol. 65, no.1, pp. 279–308, 2020. https://doi.org/10.32604/cmc.2020.011001
Citations