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 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) |
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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) |
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Keyword(1) |
Large-scale Communication Software |
Keyword(2) |
Automatic Test Cases Generation |
Keyword(3) |
Machine Learning |
Keyword(4) |
Topic Model |
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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 |
6 |
Date of Issue |
2022-07-06 (NS) |
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