{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/286"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/286","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"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 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.","abstract_html":"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.","abstract_has_math":false,"creators":["Jalbert, Kevin"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Bradbury, Jeremy S."],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-09-01","date_published":"2012-09-01","updated_at":"2026-07-24T05:35:38Z","subjects":["Machine learning","Mutation testing","Software metrics","Support vector machine","Test suite effectiveness"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/286","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Bradbury, Jeremy S."]},{"key":"dc:creator","label":"Author","values":["Jalbert, Kevin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2012-11-06T20:46:28Z","2022-03-29T17:30:06Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2012-11-06T20:46:28Z","2022-03-29T17:30:06Z"]},{"key":"dc:date.issued","label":"Date","values":["2012-09-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine learning","Mutation testing","Software metrics","Support vector machine","Test suite effectiveness"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/286"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Predicting mutation score using source code and test suite metrics"]}]}],"canonical_facts":{"dc:contributor.advisor":["Bradbury, Jeremy S."],"dc:creator":["Jalbert, Kevin"],"dc:date.accessioned":["2012-11-06T20:46:28Z","2022-03-29T17:30:06Z"],"dc:date.available":["2012-11-06T20:46:28Z","2022-03-29T17:30:06Z"],"dc:date.issued":["2012-09-01"],"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."],"dc:identifier.uri":["https://hdl.handle.net/10155/286"],"dc:language.iso":["en"],"dc:subject":["Machine learning","Mutation testing","Software metrics","Support vector machine","Test suite effectiveness"],"dc:title":["Predicting mutation score using source code and test suite metrics"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:38Z"}