Title: Neural Code Completion
Authors: Chang Liu, Xin Wang, Richard Shin, Joseph E. Gonzalez, Dawn Song
Keywords: Deep learning, Applications
Abstract:
Code completion, an essential part of modern software development, yet can bechallenging for dynamically typed programming languages.  In this paper we ex-plore the use of neural network techniques to automatically learn code completionfrom  a  large  corpus  of  dynamically  typed  JavaScript  code.   We  show  differentneural networks that leverage not only token level information but also structuralinformation,  and  evaluate  their  performance  on  different  prediction  tasks.   Wedemonstrate that our models can outperform the state-of-the-art approach, whichis based on decision tree techniques, on both next non-terminal and next terminalprediction tasks by 3.8 points and 0.5 points respectively.  We believe that neuralnetwork techniques can play a transformative role in helping software developersmanage the growing complexity of software systems, and we see this work as afirst step in that direction.
Venue: Submitted to ICLR 2017