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| 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 |
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| 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. |
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| 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 | |
| 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) | |
| Download PDF | KBSE2021-49 |
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| 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 |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 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) |
| 3rd Author's Name | |
| 3rd Author's Affiliation | () |
| 4th Author's Name | |
| 4th Author's Affiliation | () |
| 5th Author's Name | |
| 5th Author's Affiliation | () |
| 6th Author's Name | |
| 6th Author's Affiliation | () |
| 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 |
| #Pages | 6 |
| Date of Issue | 2022-03-02 (KBSE) |
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