  Predicting program properties such as names or expression types has a wide
range of applications. It can ease the task of programming and increase
programmer productivity. A major challenge when learning from programs is
$\textit{how to represent programs in a way that facilitates effective
learning}$.
  We present a $\textit{general path-based representation}$ for learning from
programs. Our representation is purely syntactic and extracted automatically.
The main idea is to represent a program using paths in its abstract syntax tree
(AST). This allows a learning model to leverage the structured nature of code
rather than treating it as a flat sequence of tokens.
  We show that this representation is general and can: (i) cover different
prediction tasks, (ii) drive different learning algorithms (for both generative
and discriminative models), and (iii) work across different programming
languages.
  We evaluate our approach on the tasks of predicting variable names, method
names, and full types. We use our representation to drive both CRF-based and
word2vec-based learning, for programs of four languages: JavaScript, Java,
Python and C\#. Our evaluation shows that our approach obtains better results
than task-specific handcrafted representations across different tasks and
programming languages.
