  Automated Program Repair (APR) techniques have shown more and more promising
results in fixing real-world bugs. Despite the effectiveness, APR techniques
still face an overfitting problem: a generated patch can be incorrect although
it passes all tests. It is time-consuming to manually evaluate the correctness
of generated patches that can pass all tests. To address this problem, many
approaches have been proposed to automatically assess the correctness of
patches generated by APR techniques. These approaches are mainly evaluated
within the cross-validation setting. However, for patches generated by a new or
unseen APR tool, users are implicitly required to manually label a significant
portion of these patches in the cross-validation setting before inferring the
remaining patches. To mitigate the issue, in this study, we propose \toolname,
the patch correctness assessment by adopting a large language model for code.
Specifically, for patches generated by a new or unseen APR tool, \toolname does
not need labeled patches of this new or unseen APR tool for training but
directly queries the large language model for code to get predictions on the
correctness labels without training. In this way, \toolname can reduce the
manual labeling effort when building a model to automatically assess the
correctness of generated patches of new APR tools. \toolname prioritizes
labeled patches from existing APR tools that exhibit semantic similarity to
those generated by new APR tools, enhancing the accuracy achieved by \toolname
for patches from new APR tools. Our experimental results showed that \toolname
can achieve an accuracy of 84.4% and an F1-score of 86.5% on average although
no labeled patch of the new or unseen APR tool is available. In addition, our
proposed technique outperformed the prior state-of-the-art by a large margin.
