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| Presentation | 2016-11-17 14:25 Software Reliability Analysis Based on Machine Learning Toru Kaise (Univ. of Hyogo)  R2016-50 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Software debugging processes are made to be effective by managing methods with reliability evaluations. The reliability is defined as the value with dependence on remaining errors. However, the true amount of errors is not observed in a debugging process, and then we need to estimate the reliability based on dynamical methods. In this paper, a dynamical modeling method for interval time between debugs is proposed. The Weibull distribution is used for the hierarchical dynamical models, and the gamma distributions are used for the scale and shape parameters. In particular, prior organized knowledge is proposed to install the hypeparameters. Bayesian computational inferences are used for the Bayesian dynamical models, and a method of the machine learning is provided for the reliability prediction. The information criterion EIC is applied to the Bayesian methodologies, in addition, the model andpriors fitted to a set of data are chosen. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Dynamical model / EIC / Prior information / Weibull distribution / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 301, R2016-50, pp. 7-11, Nov. 2016. | |
| Paper # | R2016-50 | |
| Date of Issue | 2016-11-10 (R) | |
| ISSN | Print edition: ISSN 0913-5685 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 | R2016-50 |
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| Conference Information | |
| Committee | R |
| Conference Date | 2016-11-17 - 2016-11-17 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Osaka Central Electric Club Bldg. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Reliability for semiconductors and electronics devices, Overall reliability engineering (co-organized by Reliability Engineering Association of Japan Kansai Branch) |
| Paper Information | |
| Registration To | R |
| Conference Code | 2016-11-R |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Software Reliability Analysis Based on Machine Learning |
| Sub Title (in English) | |
| Keyword(1) | Dynamical model |
| Keyword(2) | EIC |
| Keyword(3) | Prior information |
| Keyword(4) | Weibull distribution |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Toru Kaise |
| 1st Author's Affiliation | University of Hyogo (Univ. of Hyogo) |
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| Speaker | Author-1 |
| Date Time | 2016-11-17 14:25:00 |
| Presentation Time | 25 minutes |
| Registration for | R |
| Paper # | R2016-50 |
| Volume (vol) | vol.116 |
| Number (no) | no.301 |
| Page | pp.7-11 |
| #Pages | 5 |
| Date of Issue | 2016-11-10 (R) |
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