  Binary similarity analysis is critical to many code-reuse-related issues and
"1-to-1" mechanism is widely applied, where one function in a binary file is
matched against one function in a source file or binary file. However, we
discover that function mapping is a more complex problem of "1-to-n" or even
"n-to-n" due to the existence of function inlining.
  In this paper, we investigate the effect of function inlining on binary
similarity analysis. We first construct 4 inlining-oriented datasets for four
similarity analysis tasks, including code search, OSS reuse detection,
vulnerability detection, and patch presence test. Then, we further study the
extent of function inlining, the performance of existing works under function
inlining, and the effectiveness of existing inlining-simulation strategies.
Results show that the proportion of function inlining can reach nearly 70%,
while most existing works neglect it and use "1-to-1" mechanism. The mismatches
cause a 30% loss in performance during code search and a 40% loss during
vulnerability detection. Moreover, two existing inlining-simulation strategies
can only recover 60% of the inlined functions. We discover that inlining is
usually cumulative when optimization increases. Conditional inlining and
incremental inlining are suggested to design low-cost and high-coverage
inlining-simulation strategies.
