The increased availability of massive codebases, sometimes referred to as "Big Code", creates a unique opportunity for new kinds of programming tools and techniques based on statistical models. These approaches will extract useful information from existing codebases and will use that information to provide statistically likely solutions to problems that are difficult or impossible to solve with traditional techniques.

The tutorial is self-contained and will include both:

• Theory: an introduction to several machine learning models suitable for learning from programs, and
• Practice: a hands-on session showing how to apply the theory for building statistical programming tools. For this task, we will use the recently released Nice2Predict (http://nice2predict.org/) framework.