In Wappler and Lammerman’s paper, the authors applied evolutionary algorithms... goals and come up with test data to reach those...

In Wappler and Lammerman’s paper, the authors applied evolutionary algorithms derive a set of test
goals and come up with test data to reach those goals. There were two kinds of studies implemented in
a prototype testing system that supported coverage-oriented testing of object oriented software.
The first study compared random testing to evolutionary testing. It was applied to a test cluster where
the max program length was set to 20 statements, for example a class called StateCounter. Eight tests
runs were carried out. It was determined that random testing did not lead to the optimum solution,
while in evolutionary testing the optimum could be found due to the high number of candidate
In the second study, three more sophisticated measures were added into the objective functions and
applied to a test object for which test programs are hard to find. The goal is to determine if adding
more sophisticated measures is necessary for successfully finding a solution using the evolutionary
approach. The results were compared to objective functions that used a Boolean measure. This study
was applied to a test cluster that consisted of classes BinaryTree, java.lang.Integer and java.lang.Double,
classes that have a lot of methods requiring other instances as parameters. Ten experiments were
carried out. With the Boolean measure, the evolutionary algorithm was not able to find a successful
solution. With the added measure dynamic error evaluation, the algorithm was more successful (the
other two measures did not improve on dynamic error evaluation.
Summary: Optimization using ‘dynamic error evaluation’ resulted in more incontrovertible solutions
than random testing or optimization using just a Boolean measure.
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