Paper Abstract and Keywords |
Presentation |
2020-03-07 13:30
Research for improving the accuracy of program fault detection by CNN-BI system Kazuhiko Ogawa, Takako Nakatani (OUJ) KBSE2019-58 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Many researchers have done much research to improve software quality.One way to improve the quality of a program is to infer defects in the source code. The inferred bug is used to improve the quality of debug and review.There are methods for inferring defects using the results obtained from metrics, and methods for inferring defects using source code.In addition to statistical methods, techniques such as machine learning and deep learning are used to improve program accuracy.In this paper, we tried to improve the inference accuracy, which was a problem in inferring defects.We used to learn all programs as one learning model.We learned by classifying project members with similar years of experience and skills.We thought that learning could improve accuracy by performing inference using multiple models.
We conducted experiments to see if the accuracy improved. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
bug inference / convolutional nural network / image of source code / deep learning / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 467, KBSE2019-58, pp. 73-78, March 2020. |
Paper # |
KBSE2019-58 |
Date of Issue |
2020-02-28 (KBSE) |
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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KBSE2019-58 |
Conference Information |
Committee |
KBSE |
Conference Date |
2020-03-06 - 2020-03-07 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Tenbusu-Naha |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
General, Student |
Paper Information |
Registration To |
KBSE |
Conference Code |
2020-03-KBSE |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Research for improving the accuracy of program fault detection by CNN-BI system |
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bug inference |
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convolutional nural network |
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image of source code |
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deep learning |
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1st Author's Name |
Kazuhiko Ogawa |
1st Author's Affiliation |
The Open University of Japan (OUJ) |
2nd Author's Name |
Takako Nakatani |
2nd Author's Affiliation |
The Open University of Japan (OUJ) |
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Speaker |
Author-1 |
Date Time |
2020-03-07 13:30:00 |
Presentation Time |
45 minutes |
Registration for |
KBSE |
Paper # |
KBSE2019-58 |
Volume (vol) |
vol.119 |
Number (no) |
no.467 |
Page |
pp.73-78 |
#Pages |
6 |
Date of Issue |
2020-02-28 (KBSE) |
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