| Summary: | Mutation testing has traditionally been used to evaluate the effectiveness of test suites
and provide con dence in the testing process. Mutation testing involves the creation of
many versions of a program each with a single syntactic fault. A test suite is evaluated
against these program versions (i.e., mutants) in order to determine the percentage
of mutants a test suite is able to identify (i.e., mutation score). A major drawback
of mutation testing is that even a small program may yield thousands of mutants
and can potentially make the process cost prohibitive. To improve the performance
and reduce the cost of mutation testing, we proposed a machine learning approach to
predict mutation score based on a combination of source code and test suite metrics.
We conducted an empirical evaluation of our approach to evaluated its effectiveness
using eight open source software systems. === UOIT
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