abstract = "This paper proposes an adaptive strategy for enhancing
Genetic Programming-based approaches to automatic test
case generation. The main contribution of this study is
that of proposing an adaptive Evolutionary Testing
methodology for promoting the introduction of relevant
instructions into the generated test cases by means of
mutation; the instructions from which the algorithm can
choose are ranked, with their rankings being updated
every generation in accordance to the feedback obtained
from the individuals evaluated in the preceding
generation. The experimental studies developed show
that the adaptive strategy proposed improves the
algorithm's efficiency considerably, while introducing
a negligible computational overhead.",
notes = "GECCO-2009 A joint meeting of the eighteenth
international conference on genetic algorithms
(ICGA-2009) and the fourteenth annual genetic
programming conference (GP-2009).