Applying Token Tagging to Augment Dataset for Automatic Program Repair

Huimin Hu, Byungjeong Lee

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)628-636
Number of pages9
JournalJournal of Information Processing Systems
Issue number5
StatePublished - Oct 2022


  • Augment dataset
  • Automatic program repair
  • Machine learning
  • Token tagging


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