{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1612"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1612","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Test case prioritization using transfer learning in continuous integration environments","abstract":"Continuous integration (CI) systems use automated tests to verify software builds and optimize the testing process through test case prioritization (TCP). Recent research studies on TCP in CI environments have employed machine learning (ML) techniques to address the dynamic nature of CI. However, the performance of ML for TCP may decrease because of the low volume of data or less failure rate, whereas using existing data with similar patterns from other domains can be valuable. Therefore, this thesis investigates the potential of transfer learning (TL) algorithms to improve test case failure prediction and prioritization in CI environments. We conduct a comparative analysis of traditional TL algorithms to improve test failure prediction of large-scale industrial projects. Our experimental results show that parameter-based TL is most effective, and its usefulness is further emphasized by the scarcity of publicly available large-scale CI datasets due to data privacy regulations. We also present a new technique called TCP TB that prioritizes test cases using the prediction probability of test failures generated by a parameter-based transfer learning algorithm called TransBoost. We compare TCP TB’s performance with different ML approaches and CI-RTP/S on 24 study subjects and demonstrate that TCP TB outperforms them, improving TCP performance in 82.61% of the cases.","abstract_html":"Continuous integration (CI) systems use automated tests to verify software builds and optimize the testing process through test case prioritization (TCP). Recent research studies on TCP in CI environments have employed machine learning (ML) techniques to address the dynamic nature of CI. However, the performance of ML for TCP may decrease because of the low volume of data or less failure rate, whereas using existing data with similar patterns from other domains can be valuable. Therefore, this thesis investigates the potential of transfer learning (TL) algorithms to improve test case failure prediction and prioritization in CI environments. We conduct a comparative analysis of traditional TL algorithms to improve test failure prediction of large-scale industrial projects. Our experimental results show that parameter-based TL is most effective, and its usefulness is further emphasized by the scarcity of publicly available large-scale CI datasets due to data privacy regulations. We also present a new technique called TCP TB that prioritizes test cases using the prediction probability of test failures generated by a parameter-based transfer learning algorithm called TransBoost. We compare TCP TB’s performance with different ML approaches and CI-RTP/S on 24 study subjects and demonstrate that TCP TB outperforms them, improving TCP performance in 82.61% of the cases.","abstract_has_math":false,"creators":["Mamata, Rezwana"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Azim, Akramul"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-04-01","date_published":"2023-04-01","updated_at":"2026-07-24T05:35:24Z","subjects":["Test case prioritization","Transfer learning","Continuous integration"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1612","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Azim, Akramul"]},{"key":"dc:creator","label":"Author","values":["Mamata, Rezwana"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-04-25T16:34:19Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-04-25T16:34:19Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-04-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"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":["Test case prioritization","Transfer learning","Continuous integration"]}]},{"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/10155/1612"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Continuous integration (CI) systems use automated tests to verify software builds and optimize the testing process through test case prioritization (TCP). Recent research studies on TCP in CI environments have employed machine learning (ML) techniques to address the dynamic nature of CI. However, the performance of ML for TCP may decrease because of the low volume of data or less failure rate, whereas using existing data with similar patterns from other domains can be valuable. Therefore, this thesis investigates the potential of transfer learning (TL) algorithms to improve test case failure prediction and prioritization in CI environments. We conduct a comparative analysis of traditional TL algorithms to improve test failure prediction of large-scale industrial projects. Our experimental results show that parameter-based TL is most effective, and its usefulness is further emphasized by the scarcity of publicly available large-scale CI datasets due to data privacy regulations. We also present a new technique called TCP TB that prioritizes test cases using the prediction probability of test failures generated by a parameter-based transfer learning algorithm called TransBoost. We compare TCP TB’s performance with different ML approaches and CI-RTP/S on 24 study subjects and demonstrate that TCP TB outperforms them, improving TCP performance in 82.61% of the cases."]},{"key":"dc:title","label":"Title","values":["Test case prioritization using transfer learning in continuous integration environments"]}]}],"canonical_facts":{"dc:contributor.advisor":["Azim, Akramul"],"dc:creator":["Mamata, Rezwana"],"dc:date.accessioned":["2023-04-25T16:34:19Z"],"dc:date.available":["2023-04-25T16:34:19Z"],"dc:date.issued":["2023-04-01"],"dc:description.abstract":["Continuous integration (CI) systems use automated tests to verify software builds and optimize the testing process through test case prioritization (TCP). Recent research studies on TCP in CI environments have employed machine learning (ML) techniques to address the dynamic nature of CI. However, the performance of ML for TCP may decrease because of the low volume of data or less failure rate, whereas using existing data with similar patterns from other domains can be valuable. Therefore, this thesis investigates the potential of transfer learning (TL) algorithms to improve test case failure prediction and prioritization in CI environments. We conduct a comparative analysis of traditional TL algorithms to improve test failure prediction of large-scale industrial projects. Our experimental results show that parameter-based TL is most effective, and its usefulness is further emphasized by the scarcity of publicly available large-scale CI datasets due to data privacy regulations. We also present a new technique called TCP TB that prioritizes test cases using the prediction probability of test failures generated by a parameter-based transfer learning algorithm called TransBoost. We compare TCP TB’s performance with different ML approaches and CI-RTP/S on 24 study subjects and demonstrate that TCP TB outperforms them, improving TCP performance in 82.61% of the cases."],"dc:identifier.uri":["https://hdl.handle.net/10155/1612"],"dc:language.iso":["en"],"dc:subject":["Test case prioritization","Transfer learning","Continuous integration"],"dc:title":["Test case prioritization using transfer learning in continuous integration environments"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:24Z"}