Split Attention Pointer Network For Source Code Language Modeling

There is a growing interest in leveraging Deep Learning (DL) for automating Software Engineering tasks such as program completion. In this paper, we leverage Recurrent Neural Networks (RNNs) for Abstract Syntax Tree (AST)-based code completion. Our approach converts source code into AST nodes and a language model predicts the type and value attributes of next tokens. Our work demonstrates that the attention augmented RNN-based language models are able to understand local context and copy from recent past tokens which have never appeared in the training data set. We observed a drop of performances of both type and value predictions when using a traditional pointer network architecture for out-of-vocabulary (OoV) copying and context understanding, which we call multi-task conflict. To address this challenge, we have devised a new structure of self-attention called Split Attention, where two separate dot-product layers are applied to different parts of the history cache. Based on this structure, we propose a new network called Split Attention Pointer Network (SAPN), which is efficient and flexible in both learning local context and copying OoV tokens from history. The empirical results suggest that our model is superior in syntax-aware generation and OoV token prediction by demonstrating attention behavior similar to human programmers. The results also indicate that our model out performs previous state-of-the-art approaches by more than 6% on widely recognized program completion benchmarks.