DeepVS: An efficient and generic approach for source code modelling usage

Abstract

The source code suggestions provided by current integrated development environment (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 modelling of source code for various software engineering tasks. However, these techniques lack adequate generalisation and resistance to acclimate the use of such models in a real-world software development environment. This Letter presents 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 DeepVS tool is capable of suggesting zero-day (unseen) code tokens by learning coding patterns from real-world software systems.