\renewcommand{\abstractname}{\Large Abstract}
\begin{abstract}
In recent decades, \gls{apr} has been advancing consistently
according to benchmarks.  However, its use in practice is still limited
due to the difficulty in choosing a desired patch among the generated pool.

This work introduces a method to logically differentiate between patches
through symbolic execution.  The technique generates a tree of decisions
for developers to reason between patches based on the program's inputs
and semi-automatically captured outputs.  Its implementation \psychic{}
based on \klee{} is evaluated on patches automatically generated
for toy programs in the \textsc{IntroClass} benchmark,
showing promising preliminaries.
\end{abstract}
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