Applying Token Tagging to Augment Dataset for Automatic Program Repair


Huimin Hu, Byungjeong Lee, Journal of Information Processing Systems Vol. 18, No. 5, pp. 628-636, Oct. 2022  

10.3745/JIPS.04.0251
Keywords: Augment Dataset, Automatic Program Repair, Machine Learning, Token Tagging
Fulltext:

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.


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Cite this article
[APA Style]
Hu, H. & Lee, B. (2022). Applying Token Tagging to Augment Dataset for Automatic Program Repair. Journal of Information Processing Systems, 18(5), 628-636. DOI: 10.3745/JIPS.04.0251.

[IEEE Style]
H. Hu and B. Lee, "Applying Token Tagging to Augment Dataset for Automatic Program Repair," Journal of Information Processing Systems, vol. 18, no. 5, pp. 628-636, 2022. DOI: 10.3745/JIPS.04.0251.

[ACM Style]
Huimin Hu and Byungjeong Lee. 2022. Applying Token Tagging to Augment Dataset for Automatic Program Repair. Journal of Information Processing Systems, 18, 5, (2022), 628-636. DOI: 10.3745/JIPS.04.0251.