CodeGRU: Context-aware Deep Learning with Gated Recurrent Unit for Source Code Modeling

Recently many NLP-based deep learning models have been applied to model source code for source code suggestion and recommendation tasks. A major limitation of these approaches is that they take source code as simple tokens of text and ignore its contextual, syntaxtual and structural dependencies. In this work, we present CodeGRU, a Gated Recurrent Unit based source code language model that is capable of capturing contextual, syntaxtual and structural dependencies for modeling the source code. The CodeGRU introduces the following several new components. The Code Sampler is first proposed for selecting noise-free code samples and transforms obfuscate code to its proper syntax, which helps to capture syntaxtual and structural dependencies. The Code Regularize is next introduced to encode source code which helps capture the contextual dependencies of the source code. Finally, we propose a novel method which can learn variable size context for modeling source code. We evaluated CodeGRU with real-world dataset and it shows that CodeGRU can effectively capture contextual, syntaxtual and structural dependencies which previous works fails. We also discuss and visualize two use cases of CodeGRU for source code modeling tasks (1) source code suggestion, and (2) source code generation.