Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Applying Token Tagging to Augment Dataset for Automatic Program Repair

  • Received : 2022.08.23
  • Accepted : 2022.09.11
  • Published : 2022.10.31
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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.

Keywords

Acknowledgement

This work was supported by National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (No. NRF-2020R1A2B5B01002467 and NRF-2022M3J6A1084845).

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