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| 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 |
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| 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 | |
| Copyright and reproduction |
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 |
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| 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 |
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| Keyword(7) | |
| Keyword(8) | |
| 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 |
| #Pages | 6 |
| Date of Issue | 2023-07-21 (R) |
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