Code vectors: understanding programs through embedded abstracted symbolic traces
Jordan Henkel, Shuvendu K. Lahiri, Ben Liblit, Thomas Reps · 2018
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.