  Decompilers are widely used by security researchers and developers to reverse
engineer executable code. While modern decompilers are adept at recovering
instructions, control flow, and function boundaries, some useful information
from the original source code, such as variable types and names, is lost during
the compilation process. Our work aims to predict these variable types and
names from the remaining information.
  We propose STRIDE, a lightweight technique that predicts variable names and
types by matching sequences of decompiler tokens to those found in training
data. We evaluate it on three benchmark datasets and find that STRIDE achieves
comparable performance to state-of-the-art machine learning models for both
variable retyping and renaming while being much simpler and faster. We perform
a detailed comparison with two recent SOTA transformer-based models in order to
understand the specific factors that make our technique effective. We
implemented STRIDE in fewer than 1000 lines of Python and have open-sourced it
under a permissive license at https://github.com/hgarrereyn/STRIDE.
