Applying Token Tagging to Augment Dataset for Automatic Program Repair
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초록
Automatic program repair (APR) techniques focus on automatically repairing bugs in programs and providingcorrect patches for developers, which have been investigated for decades. However, most studies havelimitations in repairing complex bugs. To overcome these limitations, we developed an approach that augmentsdatasets by utilizing token tagging and applying machine learning techniques for APR. First, to alleviate thedata insufficiency problem, we augmented datasets by extracting all the methods (buggy and non-buggymethods) in the program source code and conducting token tagging on non-buggy methods. Second, we fed thepreprocessed code into the model as an input for training. Finally, we evaluated the performance of the proposedapproach by comparing it with the baselines. The results show that the proposed approach is efficient foraugmenting datasets using token tagging and is promising for APR.
Abstract
Automatic program repair (APR) techniques focus on automatically repairing bugs in programs and providingcorrect patches for developers, which have been investigated for decades. However, most studies havelimitations in repairing complex bugs. To overcome these limitations, we developed an approach that augmentsdatasets by utilizing token tagging and applying machine learning techniques for APR. First, to alleviate thedata insufficiency problem, we augmented datasets by extracting all the methods (buggy and non-buggymethods) in the program source code and conducting token tagging on non-buggy methods. Second, we fed thepreprocessed code into the model as an input for training. Finally, we evaluated the performance of the proposedapproach by comparing it with the baselines. The results show that the proposed approach is efficient foraugmenting datasets using token tagging and is promising for APR.
참고문헌 (9)
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