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September 5, 2012

Speaker: Mr. Kevin Jalbert, MSc student, Ontario Tech University

Title: Predicting Mutation Score Using Source Code and Test Suite Metrics

Abstract: 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 any 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 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 evaluate its effectiveness using eight open-source software systems. We achieved an average method-level prediction accuracy of 49.7920% using our eight test subjects. Experimentally we found a pair of configuration parameters that maximized our prediction accuracy over all our test subjects, without per-subject tuning. Finally, we demonstrated that it is not necessary to train on 90% of the available data in order to achieve near-optimal prediction accuracy.

Biography: Kevin Jalbert is a Computer Science MSc student in the Faculty of Science under the supervision of Dr. Jeremy Bradbury. He has published four peer-reviewed papers and was the recipient of the best paper award at the Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE 2012).