Automated Program Repair in the Era of Large Language Models

Automated Program Repair (APR) aims to help developers automatically patch software systems. Existing traditional and learning-based APR techniques typically rely on high-quality bug-fixing datasets to craft repair templates or directly predict potential patches based on Neural Machine Translation (NMT). Meanwhile, such bug-fixing datasets can be extremely hard to construct, limited in size, and may also contain various irrelevant/noisy commits or changes. As a result, it is hard for existing APR techniques to fix complicated bugs unseen or hard to generalize from such bug-fixing datasets.