abstract = "Symbolic execution is a powerful technique for
software testing, but suffers from limitations when
encountering external functions, such as native methods
or third-party libraries. Existing solutions often
require additional context, expensive SMT solvers, or
manual intervention to approximate these functions
through symbolic stubs. we propose a novel approach to
automatically generate symbolic stubs for external
functions during symbolic execution that leverages
Genetic Programming. When the symbolic executor
encounters an external function, AutoStub generates
training data by executing the function on randomly
generated inputs and collecting the outputs. Genetic
programming then derives expressions that approximate
the behavior of the function, serving as symbolic
stubs. These automatically generated stubs allow the
symbolic executor to continue the analysis without
manual intervention, enabling the exploration of
program paths that were previously intractable. We
demonstrate that AutoStub can automatically approximate
external functions with over 90% accuracy for 55% of
the functions evaluated, and can infer
language-specific behaviors that reveal edge cases
crucial for software testing.",
