A deep language model for software code
Existing language models such as n grams for software code often fail to capture a long context where dependent code elements scatter far apart. In this paper, we propose a novel approach to build a language model for software code to address this particular issue. Our language model, partly inspired by human memory, is built upon the powerful deep learning based Long Short Term Memory architecture that is capable of learning long term dependencies which occur frequently in software code. Results from our intrinsic evaluation on a corpus of Java projects have demonstrated the effectiveness of our language model. This work contributes to realizing our vision for DeepSoft, an end to end, generic deep learning based framework for modeling software and its development process.
FSE 2016