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Software debugging, and program repair are among the most time-consuming andlabor-intensive tasks in software engineering that would benefit a lot fromautomation. In this paper, we propose a novel automated program repair approachbased on CodeBERT, which is a transformer-based neural architecture pre-trainedon large corpus of source code. We fine-tune our model on the ManySStuBs4Jsmall and large datasets to automatically generate the fix codes. The resultsshow that our technique accurately predicts the fixed codes implemented by thedevelopers in 19-72% of the cases, depending on the type of datasets, in lessthan a second per bug. We also observe that our method can generatevaried-length fixes (short and long) and can fix different types of bugs, evenif only a few instances of those types of bugs exist in the training dataset.

Applying CodeBERT for Automated Program Repair of Java Simple Bugs