Paper Abstract and Keywords |
Presentation |
2021-07-08 14:50
Research for using image analysis of program fault by deep learning for code review. Kazuhiko Ogawa, Takako Nakatani (OUJ) SS2021-6 KBSE2021-18 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In order to predict the location of faults in a program, we imaged the source code of the defective program and verified whether we could find the defective part of the program by learning with deep learning.
We found that the descriptions of the programs that caused the defects had something in common in the appearance of the source code, and we thought that we could find the defects by applying CNN (Convolutional Neural Network), which is one of the deep learning methods.
In this paper, we compare the results of a code review of a program that uses the results of inference from a model learned by deep learning and a code review of a program that does not use the results of inference.
We will experiment to see whether the code review using the results of inference by deep learning can reduce the review time and detect more defects than the code review without the results of inference.
We will also verify whether it is possible to detect unknown faults. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
bug inference / convolutional nural network / image of source code / deep learning / code review / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 95, KBSE2021-18, pp. 31-36, July 2021. |
Paper # |
KBSE2021-18 |
Date of Issue |
2021-07-01 (SS, 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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SS2021-6 KBSE2021-18 |
Conference Information |
Committee |
KBSE IPSJ-SE SS |
Conference Date |
2021-07-08 - 2021-07-09 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online (Zoom) |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
KBSE |
Conference Code |
2021-07-KBSE-SE-SS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Research for using image analysis of program fault by deep learning for code review. |
Sub Title (in English) |
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Keyword(1) |
bug inference |
Keyword(2) |
convolutional nural network |
Keyword(3) |
image of source code |
Keyword(4) |
deep learning |
Keyword(5) |
code review |
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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 |
2021-07-08 14:50:00 |
Presentation Time |
25 minutes |
Registration for |
KBSE |
Paper # |
SS2021-6, KBSE2021-18 |
Volume (vol) |
vol.121 |
Number (no) |
no.94(SS), no.95(KBSE) |
Page |
pp.31-36 |
#Pages |
6 |
Date of Issue |
2021-07-01 (SS, KBSE) |
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