  A code completion system suggests future code elements to developers given a
partially-complete code snippet. Code completion is one of the most useful
features in Integrated Development Environments (IDEs). Currently, most code
completion techniques predict a single token at a time. In this paper, we take
a further step and discuss the probability of directly completing a whole line
of code instead of a single token. We believe suggesting longer code sequences
can further improve the efficiency of developers. Recently neural language
models have been adopted as a preferred approach for code completion, and we
believe these models can still be applied to full-line code completion with a
few improvements. We conduct our experiments on two real-world python corpora
and evaluate existing neural models based on source code tokens or syntactical
actions. The results show that neural language models can achieve acceptable
results on our tasks, with significant room for improvements.
