Predicting mutation score using source code and test suite metrics
Date
2012-09-01
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Abstract
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.
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Keywords
Machine learning, Mutation testing, Software metrics, Support vector machine, Test suite effectiveness