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Paper Abstract and Keywords
Presentation 2023-07-28 15:30
Deep Learning Approach for OSS Reliability Assessment Based on Data Preprocessing Considering the Wiener Process
Yoshinobu Tamura (Yamaguchi Univ.), Shigeru Yamada (Tottori Univ.) R2023-16
Abstract (in Japanese) (See Japanese page) 
(in English) In many open source software, the development style based on the database of general bug tracking systems is the normal development paradigm. In this paper, we propose the method of reliability assessment based on the deep learning by using the fault big data obtained from the bug tracking system. Traditionally, there are many research papers in terms of the reliability assessment method by using the fault count data prediction inspired from the time series analysis based on neural network. However, in such fault count data prediction, it has been difficult to estimate the cumulative fault detected count data. In this paper, we discuss the prediction accuracy of deep learning by using the data preprocessing based on the Wiener process.
Keyword (in Japanese) (See Japanese page) 
(in English) Open Source Software / Data Preprocessing / Wiener Process / Big Data / Deep Learning / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 141, R2023-16, pp. 33-38, July 2023.
Paper # R2023-16 
Date of Issue 2023-07-21 (R) 
ISSN Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF R2023-16

Conference Information
Committee R  
Conference Date 2023-07-28 - 2023-07-28 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Reliability Theory, Communication Network Reliability, Reliability General 
Paper Information
Registration To R 
Conference Code 2023-07-R 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep Learning Approach for OSS Reliability Assessment Based on Data Preprocessing Considering the Wiener Process 
Sub Title (in English)  
Keyword(1) Open Source Software  
Keyword(2) Data Preprocessing  
Keyword(3) Wiener Process  
Keyword(4) Big Data  
Keyword(5) Deep Learning  
1st Author's Name Yoshinobu Tamura  
1st Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
2nd Author's Name Shigeru Yamada  
2nd Author's Affiliation Tottori University (Tottori Univ.)
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Speaker Author-1 
Date Time 2023-07-28 15:30:00 
Presentation Time 25 minutes 
Registration for R 
Paper # R2023-16 
Volume (vol) vol.123 
Number (no) no.141 
Page pp.33-38 
Date of Issue 2023-07-21 (R) 

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