Code Vectors: Understanding Programs Through Embedded Abstracted Symbolic Traces

Understand

With the rise of machine learning, there is a great deal of interest in treating programs as data to be fed to learning algorithms.

• However, programs do not start off in a form that is immediately amenable to most off-the-shelf learning techniques.
• Instead, it is necessary to transform the program to a suitable representation before a learning technique can be applied.
• In this paper, we use abstractions of traces obtained from symbolic execution of a program as a representation for learning word embeddings.