Predicting mutation score using source code and test suite metrics
Kevin Jalbert, Jeremy S. Bradbury · 2012
Mutation testing has traditionally been used to evaluate the effectiveness of test suites and provide confidence 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 (mutants) in order to determine the percentage of mutants a test suite is able to identify (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 propose a machine learning approach to predict mutation score based on a combination of source code and test suite metrics.