With the emergence of publicly shareable repositories on the Web (i.e. open sourced code in GitHub), there are now billions of lines of code easily to read and learn from. With the abundance of available code artifacts, researchers have begun to apply machine learning techniques to find statistical patterns in Big Code to guide developers to understand and write code better. This breakout session will cover the applications of Big Code, including program synthesis, code search, code autocomplete, code bug fixing.