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Nanjing University of Posts and Telecommunications
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KeaRepair not only achieves an 83.64% repair rate on C/C++ vulnerabilities but also tackles unique cases that existing methods fail to address.
LLMs, when orchestrated as collaborative agents, can dramatically improve vulnerability-inducing commit identification, outperforming existing SZZ algorithms by a large margin.
