  With the advancement of deep learning techniques, the performance of
Automatic Program Repair(APR) techniques has reached a new level. Previous deep
learning-based APR techniques essentially modified program sentences in the
Autoregressive(AR) manner, which predicts future values based on past values.
Due to the manner of word-by-word generation, the AR-based APR technique has a
huge time delay. This negative consequence overshadows the widespread adoption
of APR techniques in real-life software development.
  To address the issue, we aim to apply the Non-Autoregressive(NAR) method to
the APR task, which can output target code in a parallel manner to avoid huge
inference delays. To effectively adapt the NAR manner for the APR task, we in
this paper propose NARRepair, the first customized NAR code generation model
for the APR task. The NARRepair features three major novelties, including 1)
using repair actions to alleviate the over-correction issue, 2) extracting
dependency information from AST to alleviate the issue of lacking inter-word
dependency information, 3) employing two-stage decoding to alleviate the issue
of lacking contextual information. We evaluated NARRepair on three widely used
datasets in the APR community, and the results show that our technique can
significantly improve the inference speed while maintaining high repair
accuracy.
