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Paper Abstract and Keywords
Presentation 2024-03-07 17:45
For evaluating the effectiveness of CodeT5 transfer learning in refactoring recommendations.
Yuto Nakajima, Kenji Fujiwara (Tokyo City University) SS2023-62
Abstract (in Japanese) (See Japanese page) 
(in English) Refactoring is "the process of restructuring the internal architecture of software to make it easier to understand and modify without changing its external behavior," and it is a crucial activity in software development. In this study, we utilized CodeT5, a pre-trained model specialized in source code, to perform transfer learning for refactoring recommendations, aiming to improve prediction accuracy when using deep learning models. Specifically, we fine-tuned CodeT5 as a recommendation model using a dataset comprised of over 300,000 Extract Method refactoring from 11,149 real projects across three repository groups: Apache, F-Droid, and GitHub. The model's accuracy was evaluated using Precision, Recall, and F-measure as metrics. The results showed that the model could identify 97% of the methods targeted for refactoring from the recommendation model, achieving a 13 percentage point increase in precision and a 12 percentage point increase in recall compared to the method by Aniche et al. This indicates that transfer learning using CodeT5 is effective for refactoring recommendations.
Keyword (in Japanese) (See Japanese page) 
(in English) Refactoring recommendation / deep learning / CodeT5 / pre-training model / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 414, SS2023-62, pp. 79-84, March 2024.
Paper # SS2023-62 
Date of Issue 2024-02-29 (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)
Download PDF SS2023-62

Conference Information
Committee SS  
Conference Date 2024-03-07 - 2024-03-09 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SS 
Conference Code 2024-03-SS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) For evaluating the effectiveness of CodeT5 transfer learning in refactoring recommendations. 
Sub Title (in English)  
Keyword(1) Refactoring recommendation  
Keyword(2) deep learning  
Keyword(3) CodeT5  
Keyword(4) pre-training model  
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1st Author's Name Yuto Nakajima  
1st Author's Affiliation Tokyo City University (Tokyo City University)
2nd Author's Name Kenji Fujiwara  
2nd Author's Affiliation Tokyo City University (Tokyo City University)
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Speaker Author-1 
Date Time 2024-03-07 17:45:00 
Presentation Time 25 minutes 
Registration for SS 
Paper # SS2023-62 
Volume (vol) vol.123 
Number (no) no.414 
Page pp.79-84 
#Pages 6 
Date of Issue 2024-02-29 (SS) 

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