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| Presentation | 2021-03-04 15:50 Untangling Composite Changes Using Tree-based Convolution Neural Network Cong Li, Takashi Kobayashi (Tokyo Tech)  SS2020-46 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Developers often bundle unrelated changes in a single commit, thus creating a so-called composite commit. Composite commit is problematic because it makes code review, reversion, and integration of these commits harder. Recent researches have attempted to use the information of Abstract Syntax Tree (AST) to untangling composite commits. However, they did not make full use of the AST structure information. To make full use of AST structure information to untangle a composite commit. First, we predict the relationship between two code fragments using a Tree-based CNN model, which can capture both the structural and lexical information of the code fragment. Second, we cluster these code fragments according to their relationship. Third, we evaluated whether our approach can untangle composite commits correctly. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Commit Untangling / Composed Commit / Change Partitioning / Tree-based CNN / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 407, SS2020-46, pp. 108-113, March 2021. | |
| Paper # | SS2020-46 | |
| Date of Issue | 2021-02-24 (SS) | |
| 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) | |
| Notes on Review | This article is a technical report without peer review, and its polished version will be published elsewhere. | |
| Download PDF | SS2020-46 |
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| Conference Information | |
| Committee | SS |
| Conference Date | 2021-03-03 - 2021-03-04 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | SS |
| Conference Code | 2021-03-SS |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Untangling Composite Changes Using Tree-based Convolution Neural Network |
| Sub Title (in English) | |
| Keyword(1) | Commit Untangling |
| Keyword(2) | Composed Commit |
| Keyword(3) | Change Partitioning |
| Keyword(4) | Tree-based CNN |
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| 1st Author's Name | Cong Li |
| 1st Author's Affiliation | Tokyo Institute of Technlogy (Tokyo Tech) |
| 2nd Author's Name | Takashi Kobayashi |
| 2nd Author's Affiliation | Tokyo Institute of Technlogy (Tokyo Tech) |
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| Speaker | Author-1 |
| Date Time | 2021-03-04 15:50:00 |
| Presentation Time | 25 minutes |
| Registration for | SS |
| Paper # | SS2020-46 |
| Volume (vol) | vol.120 |
| Number (no) | no.407 |
| Page | pp.108-113 |
| #Pages | 6 |
| Date of Issue | 2021-02-24 (SS) |
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