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Paper Abstract and Keywords
Presentation 2022-03-09 16:20
Code review support and verification of effectiveness using deep learning with images of programs
Kazuhiko Ogawa, Takako Nakatani (OUJ) KBSE2021-49
Abstract (in Japanese) (See Japanese page) 
(in English) Code review is one of the ways to improve the quality of programs.
Code reviews cannot point out all faults, but if reviewers can reduce the missing of point out, the quality of the system will improve.
In our research, we aim to support the review of programs by reviewers and to point out defects that reviewers cannot point out.
In order to support code review, the results of supervised learning are used to infer possible faults in the program.
Supervised learning transforms the program into an image and learns the faults in the program.
We used the results of our reasoning about the likelihood of faults to create a list for support of code review.
We conducted an experiment to verify that the number and type of faults that can be pointed out increases when reviewers refer to the list and perform code reviews.
In the experiment, we conducted reviews with and without the list, and compared and verified the results of the reviews.
As a result of the experiment, some of the program faults that reviewers pointed out in the code review using the list increased in type and number.
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. 424, KBSE2021-49, pp. 48-53, March 2022.
Paper # KBSE2021-49 
Date of Issue 2022-03-02 (KBSE) 
ISSN Online edition: ISSN 2432-6380
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 KBSE2021-49

Conference Information
Committee KBSE  
Conference Date 2022-03-09 - 2022-03-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Online (Zoom) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General, Student 
Paper Information
Registration To KBSE 
Conference Code 2022-03-KBSE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Code review support and verification of effectiveness using deep learning with images of programs 
Sub Title (in English)  
Keyword(1) bug inference  
Keyword(2) convolutional nural network  
Keyword(3) image of source code  
Keyword(4) deep learning  
Keyword(5) code review  
1st Author's Name Kazuhiko Ogawa  
1st Author's Affiliation Open University of Japan (OUJ)
2nd Author's Name Takako Nakatani  
2nd Author's Affiliation Open University of Japan (OUJ)
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Speaker Author-1 
Date Time 2022-03-09 16:20:00 
Presentation Time 30 minutes 
Registration for KBSE 
Paper # KBSE2021-49 
Volume (vol) vol.121 
Number (no) no.424 
Page pp.48-53 
Date of Issue 2022-03-02 (KBSE) 

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