JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Applying Token Tagging to Augment Dataset for Automatic Program Repair |
| 영문제목(English Title) |
Applying Token Tagging to Augment Dataset for Automatic Program Repair |
| 저자(Author) |
Huimin Hu
Byungjeong Lee
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| 원문수록처(Citation) |
VOL 18 NO. 05 PP. 0628 ~ 0636 (2022. 10) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Automatic program repair (APR) techniques focus on automatically repairing bugs in programs and providing correct patches for developers, which have been investigated for decades. However, most studies have limitations in repairing complex bugs. To overcome these limitations, we developed an approach that augments datasets by utilizing token tagging and applying machine learning techniques for APR. First, to alleviate the data insufficiency problem, we augmented datasets by extracting all the methods (buggy and non-buggy methods) in the program source code and conducting token tagging on non-buggy methods. Second, we fed the preprocessed code into the model as an input for training. Finally, we evaluated the performance of the proposed approach by comparing it with the baselines. The results show that the proposed approach is efficient for augmenting datasets using token tagging and is promising for APR. |
| 키워드(Keyword) |
Augment Dataset
Automatic Program Repair
Machine Learning
Token Tagging
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| 파일첨부 |
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