Applying CodeBERT for Automated Program Repair of Java Simple Bugs

Understand

Software debugging, and program repair are among the most time-consuming and labor-intensive tasks in software engineering that would benefit a lot from automation.

• In this paper, we propose a novel automated program repair approach based on CodeBERT, which is a transformer-based neural architecture pre-trained on large corpus of source code.
• We fine-tune our model on the ManySStuBs4J small and large datasets to automatically generate the fix codes.
• The results show that our technique accurately predicts the fixed codes implemented by the developers in 19-72% of the cases, depending on the type of datasets, in less than a second per bug.