DeepSoft: a vision for a deep model of software

Although software analytics has experienced rapid growth as a research area, it has not yet reached its full poten-tial for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for DeepSoft, an end-to-end generic framework for modeling software and its de-velopment process to predict future risks and recommend interventions. DeepSoft, 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 temporal dependencies that occur in software evolu-tion. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and auto-matically generating code patches from bug reports.