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Paper Abstract and Keywords
Presentation 2022-07-15 10:40
A Method for Selecting Training Data using Topic Models and Doc2Vec for Automatic Test Cases Generation
Yuto Fujita, Kiyoshi Ueda (Nihon Univ.) NS2022-52
Abstract (in Japanese) (See Japanese page) 
(in English) In the development of large-scale communication software, due to the increase in development cost and shortage of manpower, a method to automatically generate test cases of system testing from requirement specifications using machine learning has been studied.
In this study, we improve the accuracy of automatic test item generation by selecting requirement specifications for machine learning training data.
Each requirement specification is vectorized using topic models (LSA, LDA) and Doc2Vec, and the similarity between each requirement specification vector is calculated.
We propose a method to select specifications with high similarity to the requirement specifications of test data and use them as training data.
We evaluate the effectiveness of the proposed method by measuring the percentage of correct answers using the proposed method on 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 / Topic Model / Doc2Vec / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 105, NS2022-52, pp. 121-126, July 2022.
Paper # NS2022-52 
Date of Issue 2022-07-06 (NS) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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 NS2022-52

Conference Information
Committee NS SR RCS SeMI RCC  
Conference Date 2022-07-13 - 2022-07-15 
Place (in Japanese) (See Japanese page) 
Place (in English) The Kanazawa Theatre + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Distributed Wireless Network, M2M (Machine-to-Machine),D2D (Device-to-Device),IoT(Internet of Things), etc 
Paper Information
Registration To NS 
Conference Code 2022-07-NS-SR-RCS-SeMI-RCC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Method for Selecting Training Data using Topic Models and Doc2Vec for Automatic Test Cases Generation 
Sub Title (in English)  
Keyword(1) Large-scale Communication Software  
Keyword(2) Automatic Test Cases Generation  
Keyword(3) Machine Learning  
Keyword(4) Topic Model  
Keyword(5) Doc2Vec  
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1st Author's Name Yuto Fujita  
1st Author's Affiliation Nihon University (Nihon Univ.)
2nd Author's Name Kiyoshi Ueda  
2nd Author's Affiliation Nihon University (Nihon Univ.)
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Speaker Author-1 
Date Time 2022-07-15 10:40:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2022-52 
Volume (vol) vol.122 
Number (no) no.105 
Page pp.121-126 
#Pages
Date of Issue 2022-07-06 (NS) 


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