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
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OOSHDU.10155/286 |
| Date | 01 September 2012 |
| Creators | Jalbert, Kevin |
| Contributors | Bradbury, Jeremy S. |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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