Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques |
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Authors: | Kun Zhu Nana Zhang Qing Zhang Shi Ying Xu Wang |
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Affiliation: | 1.School of Computer Science, Wuhan University, Wuhan, 430072, China.
2 School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.
3 Department of Computer Science, Vrije University Amsterdam, Amsterdam, 1081HV, The Netherlands. |
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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. |
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Keywords: | Software defect prediction non-linear manifold learning denoising autoencoder deep neural network loss function deep learning |
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