  Code completion tools are frequently used by software developers to
accelerate software development by suggesting the following code elements.
Completing a sequence of code tokens (e.g., a full line of code) has been
proved more efficient than predicting a single token at a time. To complete the
code sequence, researchers are employing AutoRegressive (AR) decoders to
generate tokens in a left-to-right, token-by-token fashion. Consequently, the
prediction of the next token depends on all previously generated tokens, which
leads to high latency in inference. To improve the efficiency and accuracy of
full-line code completion, in this paper, we propose a Non-AutoRegressive (NAR)
model for code completion boosted by a syntax-aware sampling strategy. Our
experimental results on two widely used datasets suggest that our model
outperforms both AR and NAR baselines on full-line code completion, and it is
faster than the AR model with up to 9 times speed-up.
