  Understanding binary code is an essential but complex software engineering
task for reverse engineering, malware analysis, and compiler optimization.
Unlike source code, binary code has limited semantic information, which makes
it challenging for human comprehension. At the same time, compiling source to
binary code, or transpiling among different programming languages (PLs) can
provide a way to introduce external knowledge into binary comprehension. We
propose to develop Artificial Intelligence (AI) models that aid human
comprehension of binary code. Specifically, we propose to incorporate domain
knowledge from large corpora of source code (e.g., variable names, comments) to
build AI models that capture a generalizable representation of binary code.
Lastly, we will investigate metrics to assess the performance of models that
apply to binary code by using human studies of comprehension.
