A comparative study of deep learning techniques in software fault prediction

The University of Da Nang, Journal of Science and Technology; Voll.22, No.6B; pp: 01-05.

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Asıl Yazarlar:	Ha, Thi Minh Phuong, Nguyen, Thi Kim Ngan, Nguyen, Thanh Binh
Materyal Türü:	Bài viết
Dil:	English
Baskı/Yayın Bilgisi:	The University of Da Nang, Journal of Science and Technology 2024
Konular:	Software engineering deep learning software fault prediction abstract syntax tree software faults
Online Erişim:	https://jst-ud.vn/jst-ud/article/view/9255/6222 https://elib.vku.udn.vn/handle/123456789/4058
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Thư viện lưu trữ:	Trường Đại học Công nghệ Thông tin và Truyền thông Việt Hàn - Đại học Đà Nẵng

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spelling	oai:elib.vku.udn.vn:123456789-40582024-08-01T03:19:07Z A comparative study of deep learning techniques in software fault prediction Ha, Thi Minh Phuong Nguyen, Thi Kim Ngan Nguyen, Thanh Binh Software engineering deep learning software fault prediction abstract syntax tree software faults The University of Da Nang, Journal of Science and Technology; Voll.22, No.6B; pp: 01-05. Software fault prediction (SFP) is an important approach in software engineering that ensures software quality and reliability. Prediction of software faults helps developers identify faulty components in software systems. Several studies focus on software metrics which are input into machine learning models to predict faulty components. However, such studies may not capture the semantic and structural information of software that is necessary for building fault prediction models with better performance. Therefore, this paper discusses the effectiveness of deep learning models including Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM) that are utilized to construct fault prediction models based on the contextual information. The experiment, which has been conducted on seven Apache datasets, with Precision, Recall, and F1-score are performance metrics. The comparison results show that LSTM and RNN are potential techniques for building highly accurate fault prediction models. 2024-08-01T03:19:04Z 2024-08-01T03:19:04Z 2024-06 Working Paper 1859-1531 https://jst-ud.vn/jst-ud/article/view/9255/6222 https://elib.vku.udn.vn/handle/123456789/4058 en application/pdf The University of Da Nang, Journal of Science and Technology
institution	Trường Đại học Công nghệ Thông tin và Truyền thông Việt Hàn - Đại học Đà Nẵng
collection	DSpace
language	English
topic	Software engineering deep learning software fault prediction abstract syntax tree software faults
spellingShingle	Software engineering deep learning software fault prediction abstract syntax tree software faults Ha, Thi Minh Phuong Nguyen, Thi Kim Ngan Nguyen, Thanh Binh A comparative study of deep learning techniques in software fault prediction
description	The University of Da Nang, Journal of Science and Technology; Voll.22, No.6B; pp: 01-05.
format	Working Paper
author	Ha, Thi Minh Phuong Nguyen, Thi Kim Ngan Nguyen, Thanh Binh
author_facet	Ha, Thi Minh Phuong Nguyen, Thi Kim Ngan Nguyen, Thanh Binh
author_sort	Ha, Thi Minh Phuong
title	A comparative study of deep learning techniques in software fault prediction
title_short	A comparative study of deep learning techniques in software fault prediction
title_full	A comparative study of deep learning techniques in software fault prediction
title_fullStr	A comparative study of deep learning techniques in software fault prediction
title_full_unstemmed	A comparative study of deep learning techniques in software fault prediction
title_sort	comparative study of deep learning techniques in software fault prediction
publisher	The University of Da Nang, Journal of Science and Technology
publishDate	2024
url	https://jst-ud.vn/jst-ud/article/view/9255/6222 https://elib.vku.udn.vn/handle/123456789/4058
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A comparative study of deep learning techniques in software fault prediction

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