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authorNguyễn Gia Phong <cnx@loang.net>2023-12-11 16:45:06 +0900
committerNguyễn Gia Phong <cnx@loang.net>2023-12-11 16:45:06 +0900
commitda9e28add29b71e0b935101d9ac367cced746d76 (patch)
tree09406818f6d24e94a2bf4ac49e17b660031dbce8 /abstract.tex
parent4ae5fdc7d2e7c184709988578d12e1ba536c51dc (diff)
downloadthesis-da9e28add29b71e0b935101d9ac367cced746d76.tar.gz
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 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.
+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}
 \thispagestyle{empty}