@Article{cmc.2020.011415,
AUTHOR = {Kun Zhu, Nana Zhang, Qing Zhang, Shi Ying, Xu Wang},
TITLE = {Software Defect Prediction Based on Non-Linear Manifold  Learning and Hybrid Deep Learning Techniques},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1467--1486},
URL = {http://www.techscience.com/cmc/v65n2/39888},
ISSN = {1546-2226},
ABSTRACT = {Software defect prediction plays a very important role in software quality 
assurance, which aims to inspect as many potentially defect-prone software modules as 
possible. However, the performance of the prediction model is susceptible to high 
dimensionality of the dataset that contains irrelevant and redundant features. In addition, 
software metrics for software defect prediction are almost entirely traditional features 
compared to the deep semantic feature representation from deep learning techniques. To 
address these two issues, we propose the following two solutions in this paper: (1) We 
leverage a novel non-linear manifold learning method - SOINN Landmark Isomap (SLIsomap) to extract the representative features by selecting automatically the reasonable 
number and position of landmarks, which can reveal the complex intrinsic structure 
hidden behind the defect data. (2) We propose a novel defect prediction model named 
DLDD based on hybrid deep learning techniques, which leverages denoising autoencoder 
to learn true input features that are not contaminated by noise, and utilizes deep neural 
network to learn the abstract deep semantic features. We combine the squared error loss 
function of denoising autoencoder with the cross entropy loss function of deep neural 
network to achieve the best prediction performance by adjusting a hyperparameter. We 
compare the SL-Isomap with seven state-of-the-art feature extraction methods and 
compare the DLDD model with six baseline models across 20 open source software 
projects. The experimental results verify that the superiority of SL-Isomap and DLDD on 
four evaluation indicators.},
DOI = {10.32604/cmc.2020.011415}
}