Predicting mutation score using source code and test suite metrics

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.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2012

Author dc:creator

• Jalbert, Kevin

Advisor dc:contributor.advisor

• Bradbury, Jeremy S.

• Machine learning
• Mutation testing
• Software metrics
• Support vector machine
• Test suite effectiveness

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/286

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/286