Neural Code Completion
Chang Liu, Xin Wang, Richard Shin, Joseph E. Gonzalez, Dawn Song. 2016
Code completion, an essential part of modern software development, yet can be challenging for dynamically typed programming languages. In this paper we explore the use of neural network techniques to automatically learn code completion from a large corpus of dynamically typed JavaScript code. We show different neural networks that leverage not only token level information but also structural information, and evaluate their performance on different prediction tasks. We demonstrate that our models can outperform the state-of-the-art approach, which is based on decision tree techniques, on both next non-terminal and next terminal prediction tasks by 3.8 points and 0.5 points respectively. We believe that neural network techniques can play a transformative role in helping software developers manage the growing complexity of software systems, and we see this work as a first step in that direction.
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