@Article{cmc.2020.011001,
AUTHOR = {Nana Zhang, Kun Zhu, Shi Ying, Xu Wang},
TITLE = {Software Defect Prediction Based on Stacked Contractive  Autoencoder and Multi-Objective Optimization},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {279--308},
URL = {http://www.techscience.com/cmc/v65n1/39566},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.011001}
}