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
Hlavní autoři:	Zhu, Kun, Zhang, Nana, Ying, Shi, Zhu, Dandan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 01.06.2020
Témata:	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) 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
ISSN:	1751-8806, 1751-8814, 1751-8814
On-line přístup:	Získat plný text
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