dc.contributor.advisor | Bradbury, Jeremy S. | |
dc.contributor.author | Jalbert, Kevin | |
dc.date.accessioned | 2012-11-06T20:46:28Z | |
dc.date.accessioned | 2022-03-29T17:30:06Z | |
dc.date.available | 2012-11-06T20:46:28Z | |
dc.date.available | 2022-03-29T17:30:06Z | |
dc.date.issued | 2012-09-01 | |
dc.identifier.uri | https://hdl.handle.net/10155/286 | |
dc.description.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. | en |
dc.description.sponsorship | University of Ontario Institute of Technology | en |
dc.language.iso | en | en |
dc.subject | Machine learning | en |
dc.subject | Mutation testing | en |
dc.subject | Software metrics | en |
dc.subject | Support vector machine | en |
dc.subject | Test suite effectiveness | en |
dc.title | Predicting mutation score using source code and test suite metrics | en |
dc.type | Thesis | en |
dc.degree.level | Master of Science (MSc) | en |
dc.degree.discipline | Computer Science | en |