Code vectors: understanding programs through embedded abstracted symbolic traces
Abstract
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.
BibTeX
@inproceedings{Henkel-al:FSE18,
author = {Jordan Henkel and
Shuvendu K. Lahiri and
Ben Liblit and
Thomas W. Reps},
title = {Code vectors: understanding programs through embedded abstracted symbolic traces},
booktitle = {{ESEC/SIGSOFT} {FSE}},
pages = {163--174},
publisher = {{ACM}},
year = {2018},
}