  A mutant is a program obtained by syntactically modifying a program's source
code; an equivalent mutant is a mutant, which is functionally equivalent to the
original program. Mutants are primarily used in \emph{mutation testing}, and
when deriving a test suite, obtaining an equivalent mutant is considered to be
highly negative, although these equivalent mutants could be used for other
purposes. We present an approach that considers equivalent mutants valuable,
and utilizes them for source code optimization. Source code optimization
enhances a program's source code preserving its behavior. We showcase a
procedure to achieve source code optimization based on equivalent mutants and
discuss proper mutation operators. Experimental evaluation with Java and C
programs demonstrates the applicability of the proposed approach. An
algorithmic approach for source code optimization using equivalent mutants is
proposed. It is showcased that whenever applicable, the approach can outperform
traditional compiler optimizations.
