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| Presentation | 2023-04-14 11:15 Automatic Test Cases Generation Method by Structuring Specification Documents using Deep Learning Kiyoshi Ueda, Yuya Sato (Nihon Univ.)  NS2023-8 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In the development of large-scale communication software, problems such as high development costs and insufficient manpower have led to research into methods for automatically generating test cases of the system testing by machine learning. We have been trying to improve the accuracy of requirement specifications by learning and automatically structuring them using CRF, a machine learning machine. In order to further improve the accuracy of automatic structuring of requirement specifications, we thought that learning and automatic structuring using deep learning, which has been attracting attention in recent years, could achieve even higher accuracy. Therefore, we used deep learning, in which the computer itself searches for and learns features without specifying them. We evaluated the effectiveness of the proposed method by evaluating the correctness rate using the proposed method on actual requirement specifications of large-scale communication software. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Large-scale Communication Software / Automatic Test Cases Generation / Machine Learning / Deep Learning / LSTM / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 2, NS2023-8, pp. 38-43, April 2023. | |
| Paper # | NS2023-8 | |
| Date of Issue | 2023-04-06 (NS) | |
| 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 | NS2023-8 |
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| Conference Information | |
| Committee | NS |
| Conference Date | 2023-04-13 - 2023-04-14 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nihon University, Koriyama Campus + Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Communication traffic theory, Traffic and quality evaluation, Network performance evaluation, QoS/QoE, Reliability and robustness, Traffic and quality management, AI and machine learning, Network and system operation management, High capacity, low latency, many connections, General |
| Paper Information | |
| Registration To | NS |
| Conference Code | 2023-04-NS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Automatic Test Cases Generation Method by Structuring Specification Documents using Deep Learning |
| Sub Title (in English) | |
| Keyword(1) | Large-scale Communication Software |
| Keyword(2) | Automatic Test Cases Generation |
| Keyword(3) | Machine Learning |
| Keyword(4) | Deep Learning |
| Keyword(5) | LSTM |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Kiyoshi Ueda |
| 1st Author's Affiliation | Nihon University (Nihon Univ.) |
| 2nd Author's Name | Yuya Sato |
| 2nd Author's Affiliation | Nihon University (Nihon Univ.) |
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| Speaker | Author-1 |
| Date Time | 2023-04-14 11:15:00 |
| Presentation Time | 25 minutes |
| Registration for | NS |
| Paper # | NS2023-8 |
| Volume (vol) | vol.123 |
| Number (no) | no.2 |
| Page | pp.38-43 |
| #Pages | 6 |
| Date of Issue | 2023-04-06 (NS) |
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