RepairAgent, an autonomous agent powered by a large language model, can effectively fix real-world software bugs by dynamically interleaving information gathering, repair ingredient search, and fix validation.
Aligning the output format of large language models (LLMs) to their pre-training objective and allowing them to locate and repair bugs simultaneously can significantly improve their automated program repair (APR) performance without relying on fault localization tools.
ChatGPT, a prominent large language model, demonstrates impressive capabilities in automatically repairing buggy programs from a novel benchmark dataset, EvalGPTFix, outperforming state-of-the-art models.