This paper introduces a novel method, called WheaCha, for explaining the predictions of code models. Similar to attribution methods, WheaCha seeks to identify input features that are responsible for a particular prediction that models make. On the ...

We address the problem of reverse engineering of stripped executables, which contain no debug information. This is a challenging problem because of the low amount of syntactic information available in stripped executables, and the diverse assembly code ...

We present a neural model for representing snippets of code as continuous distributed vectors (``code embeddings''). The main idea is to represent a code snippet as a single fixed-length code vector, which can be used to predict semantic properties of ...