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2022

Practical Program Repair in the Era of Large Pre-trained Language Models

Xia, Chunqiu Steven, Wei, Yuxiang, Zhang, Lingming

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Automated Program Repair (APR) aims to help developers automatically patch software bugs.

However, current state-of-the-art traditional and learning-based APR techniques face the problem of limited patch variety, failing to fix complicated bugs.
This is mainly due to the reliance on bug-fixing datasets to craft fix templates or directly predict potential patches.
Large Pre-Trained Language Models (PLMs), trained using billions of text/code tokens, can potentially help avoid this issue.

Practical Program Repair in the Era of Large Pre-trained Language Models · Around

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