Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network
Just-in-time defect prediction is an important and useful branch in software defect prediction. At present, deep learning is a research hotspot in the field of artificial intelligence, which can combine basic defect features into deep semantic features and make up for the shortcomings of machine lea...
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| Vydáno v: | IET software Ročník 14; číslo 3; s. 185 - 195 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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The Institution of Engineering and Technology
01.06.2020
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| ISSN: | 1751-8806, 1751-8814, 1751-8814 |
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| Abstract | Just-in-time defect prediction is an important and useful branch in software defect prediction. At present, deep learning is a research hotspot in the field of artificial intelligence, which can combine basic defect features into deep semantic features and make up for the shortcomings of machine learning algorithms. However, the mainstream deep learning techniques have not been applied yet in just-in-time defect prediction. Therefore, the authors propose a novel just-in-time defect prediction model named DAECNN-JDP based on denoising autoencoder and convolutional neural network in this study, which has three main advantages: (i) Different weights for the position vector of each dimension feature are set, which can be automatically trained by adaptive trainable vector. (ii) Through the training of denoising autoencoder, the input features that are not contaminated by noise can be obtained, thus learning more robust feature representation. (iii) The authors leverage a powerful representation-learning technique, convolution neural network, to construct the basic change features into the abstract deep semantic features. To evaluate the performance of the DAECNN-JDP model, they conduct extensive within-project and cross-project defect prediction experiments on six large open source projects. The experimental results demonstrate that the superiority of DAECNN-JDP on five evaluation metrics. |
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| AbstractList | Just‐in‐time defect prediction is an important and useful branch in software defect prediction. At present, deep learning is a research hotspot in the field of artificial intelligence, which can combine basic defect features into deep semantic features and make up for the shortcomings of machine learning algorithms. However, the mainstream deep learning techniques have not been applied yet in just‐in‐time defect prediction. Therefore, the authors propose a novel just‐in‐time defect prediction model named DAECNN‐JDP based on denoising autoencoder and convolutional neural network in this study, which has three main advantages: (i) Different weights for the position vector of each dimension feature are set, which can be automatically trained by adaptive trainable vector. (ii) Through the training of denoising autoencoder, the input features that are not contaminated by noise can be obtained, thus learning more robust feature representation. (iii) The authors leverage a powerful representation‐learning technique, convolution neural network, to construct the basic change features into the abstract deep semantic features. To evaluate the performance of the DAECNN‐JDP model, they conduct extensive within‐project and cross‐project defect prediction experiments on six large open source projects. The experimental results demonstrate that the superiority of DAECNN‐JDP on five evaluation metrics. Just‐in‐time defect prediction is an important and useful branch in software defect prediction. At present, deep learning is a research hotspot in the field of artificial intelligence, which can combine basic defect features into deep semantic features and make up for the shortcomings of machine learning algorithms. However, the mainstream deep learning techniques have not been applied yet in just‐in‐time defect prediction. Therefore, the authors propose a novel just‐in‐time defect prediction model named DAECNN‐JDP based on denoising autoencoder and convolutional neural network in this study, which has three main advantages: (i) Different weights for the position vector of each dimension feature are set, which can be automatically trained by adaptive trainable vector. (ii) Through the training of denoising autoencoder, the input features that are not contaminated by noise can be obtained, thus learning more robust feature representation. (iii) The authors leverage a powerful representation‐learning technique, convolution neural network, to construct the basic change features into the deep semantic features. To evaluate the performance of the DAECNN‐JDP model, they conduct extensive within‐project and cross‐project defect prediction experiments on six large open source projects. The experimental results demonstrate that the superiority of DAECNN‐JDP on five evaluation metrics. |
| Author | Zhu, Dandan Ying, Shi Zhu, Kun Zhang, Nana |
| Author_xml | – sequence: 1 givenname: Kun surname: Zhu fullname: Zhu, Kun organization: 1School of Computer Science, Wuhan University, No.299 Bayi Road, Wuhan, People's Republic of China – sequence: 2 givenname: Nana surname: Zhang fullname: Zhang, Nana organization: 1School of Computer Science, Wuhan University, No.299 Bayi Road, Wuhan, People's Republic of China – sequence: 3 givenname: Shi surname: Ying fullname: Ying, Shi email: yingshl@whu.edu.cn organization: 1School of Computer Science, Wuhan University, No.299 Bayi Road, Wuhan, People's Republic of China – sequence: 4 givenname: Dandan surname: Zhu fullname: Zhu, Dandan organization: 2Artificial Intelligence Institute, Shanghai Jiaotong University, No.800 Dongchuan Road, Shanghai, People's Republic of China |
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| Keywords | mainstream deep learning techniques just-in-time defect prediction model cross-project defect prediction experiments denoising autoencoder basic defect features learning (artificial intelligence) autoencoder convolutional neural network neural nets software defect prediction convolution neural network |
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| Snippet | Just-in-time defect prediction is an important and useful branch in software defect prediction. At present, deep learning is a research hotspot in the field of... Just‐in‐time defect prediction is an important and useful branch in software defect prediction. At present, deep learning is a research hotspot in the field of... |
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| StartPage | 185 |
| SubjectTerms | autoencoder convolutional neural network basic defect features convolution neural network cross-project defect prediction experiments denoising autoencoder just-in-time defect prediction model learning (artificial intelligence) mainstream deep learning techniques neural nets software defect prediction Special Issue: Knowledge Discovery for Software Development (KDSD) |
| Title | Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network |
| URI | http://digital-library.theiet.org/content/journals/10.1049/iet-sen.2019.0278 https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fiet-sen.2019.0278 |
| Volume | 14 |
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