A deep language model for software code
This work addresses a specific bottleneck in software code modeling for developers and researchers, though it appears incremental as it builds on existing LSTM methods.
The authors tackled the problem of existing language models failing to capture long-range dependencies in software code by proposing a novel deep learning-based model using Long Short Term Memory architecture, which demonstrated effectiveness in intrinsic evaluations on a Java corpus.
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.