  The source code suggestions provided by current IDEs are mostly dependent on
static type learning. These suggestions often end up proposing irrelevant
suggestions for a peculiar context. Recently, deep learning-based approaches
have shown great potential in the modeling of source code for various software
engineering tasks. However, these techniques lack adequate generalization and
resistance to acclimate the use of such models in a real-world software
development environment. This letter presents \textit{DeepVS}, an end-to-end
deep neural code completion tool that learns from existing codebases by
exploiting the bidirectional Gated Recurrent Unit (BiGRU) neural net. The
proposed tool is capable of providing source code suggestions instantly in an
IDE by using pre-trained BiGRU neural net. The evaluation of this work is
two-fold, quantitative and qualitative. Through extensive evaluation on ten
real-world open-source software systems, the proposed method shows significant
performance enhancement and its practicality. Moreover, the results also
suggest that \textit{DeepVS} tool is capable of suggesting zero-day (unseen)
code tokens by learning coding patterns from real-world software systems.
