Non-autoregressive Model for Full-line Code Completion

Fang Liu; Zhiyi Fu; Ge Li; Zhi Jin; Hui Liu; Yiyang Hao

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arXiv · 2204.09877

Non-autoregressive Model for Full-line Code Completion

Fang Liu·Zhiyi Fu·Ge Li·Zhi Jin·Hui Liu·Yiyang Hao

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Abstract

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.

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BibTeX RIS

Fang Liu, Zhiyi Fu, Ge Li, Zhi Jin, Hui Liu, Yiyang Hao. 2022-04-21. Non-autoregressive Model for Full-line Code Completion. https://arxiv.org/abs/2204.09877

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