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Recently deep learning based Natural Language Processing (NLP) models haveshown great potential in the modeling of source code. However, a majorlimitation of these approaches is that they take source code as simple tokensof text and ignore its contextual, syntactical and structural dependencies. Inthis work, we present CodeGRU, a gated recurrent unit based source codelanguage model that is capable of capturing source code's contextual,syntactical and structural dependencies. We introduce a novel approach whichcan capture the source code context by leveraging the source code token types.Further, we adopt a novel approach which can learn variable size context bytaking into account source code's syntax, and structural information. Weevaluate CodeGRU with real-world data set and it shows that CodeGRU outperformsthe state-of-the-art language models and help reduce the vocabulary size up to24.93\%. Unlike previous works, we tested CodeGRU with an independent test setwhich suggests that our methodology does not requisite the source code comesfrom the same domain as training data while providing suggestions. We furtherevaluate CodeGRU with two software engineering applications: source codesuggestion, and source code completion. Our experiment confirms that the sourcecode's contextual information can be vital and can help improve the softwarelanguage models. The extensive evaluation of CodeGRU shows that it outperformsthe state-of-the-art models. The results further suggest that the proposedapproach can help reduce the vocabulary size and is of practical use forsoftware developers.

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