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Abstract
En 中文
Code completion, one of the most useful features in the Integrated Development Environments (IDEs), can accelerate software development by suggesting the next probable tokens based on existing code in real-time. Recent studies have shown that recurrent neural networks based statistical language models can improve the performance of code completion tools through learning from large-scale software repositories. However, most of the existing approaches treat code completion as a single generation task in which the model predicts the value of the tokens or AST nodes based on the contextual source code without considering the syntactic constraints such as the static type information. Besides, the semantic relationships in programs can be very long. Existing recurrent neural networks based language models are not sufficient to model the long-term dependency. In this paper, we tackle the aforementioned limitations by building a unified multi-task learning based code completion model for both AST-level and token-level code completion. To model the relationship and constraints between the type and value of the code elements, we adopt a multi-task learning framework to predict the type and value of the tokens (AST nodes) simultaneously. To capture the long-term dependency in the input programs, we employ a self-attentional architecture based network as the base language model. We apply our approach to both AST-level and token-level code completion. Experimental results demonstrate the effectiveness of our model when compared with state-of-the-art methods.
Keywords:
Code completion
Deep learning
Multi-task learning
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3.6
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2.0K
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