{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124275"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124275","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"When automated program repair meets regression testing – An extensive study on 2 million patches","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["Yang, Jun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhang, Lingming"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Automated Program Repair","Regression Testing"],"languages":["en","eng"],"rights":["Copyright 2024 Jun Yang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124275","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhang, Lingming"]},{"key":"dc:creator","label":"Author","values":["Yang, Jun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-16"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Automated Program Repair","Regression Testing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Jun Yang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124275"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Jun Yang, accepted the attached license on 2024-04-13 at 10:37.","The student, Jun Yang, submitted this Thesis for approval on 2024-04-13 at 12:58.","This Thesis was approved for publication on 2024-04-16 at 15:44.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20395 on 2024-09-16 at 00:34:12","In recent years, Automated Program Repair (APR) has been extensively studied in academia and even drawn wide attention from industry. However, APR techniques can be extremely time consuming since (1) a large number of patches can be generated for a given bug, and (2) each patch needs to be executed on the original tests to ensure its correctness. In the literature, various techniques (e.g., based on learning, mining, and constraint solving) have been proposed/studied to reduce the number of patches. Intuitively, every patch can be treated as a software revision during regression testing; thus, traditional Regression Test Selection (RTS) techniques can be leveraged to only execute the tests affected by each patch (as the other tests would keep the same outcomes) to further reduce patch execution time. However, few APR systems actually adopt RTS and there is still a lack of systematic studies demonstrating the benefits of RTS and the impact of different RTS strategies on APR. To this end, this paper presents the first extensive study of widely-used RTS techniques at different levels (i.e., class/method/statement levels) for 12 state-of-the- art APR systems on over 2M patches. Our study reveals various practical guidelines for bridging the gap between APR and regression testing, including: (1) the number of patches widely used for measuring APR efficiency can incur skewed conclusions, and the use of inconsistent RTS configurations can further skew the conclusions; (2) all studied RTS techniques can substantially improve APR efficiency and should be considered in future APR work; (3) method- and statement-level RTS outperform class-level RTS substantially, and should be preferred; (4) RTS techniques can substantially outperform state-of-the-art test prioritization techniques for APR, and combining them can further improve APR efficiency; and (5) traditional Regression Test Prioritization (RTP) widely studied in regression testing performs even better than APR-specific test prioritization when combined with most RTS techniques. Furthermore, we also present the detailed impact of different patch categories and patch validation strategies on our findings. Furthermore, we also present the detailed impact of different patch categories and patch validation strategies on our findings."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["When automated program repair meets regression testing – An extensive study on 2 million patches"]}]}],"canonical_facts":{"dc:contributor":["Zhang, Lingming"],"dc:creator":["Yang, Jun"],"dc:date":["2024-05","2024-04-16"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Jun Yang, accepted the attached license on 2024-04-13 at 10:37.","The student, Jun Yang, submitted this Thesis for approval on 2024-04-13 at 12:58.","This Thesis was approved for publication on 2024-04-16 at 15:44.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20395 on 2024-09-16 at 00:34:12","In recent years, Automated Program Repair (APR) has been extensively studied in academia and even drawn wide attention from industry. However, APR techniques can be extremely time consuming since (1) a large number of patches can be generated for a given bug, and (2) each patch needs to be executed on the original tests to ensure its correctness. In the literature, various techniques (e.g., based on learning, mining, and constraint solving) have been proposed/studied to reduce the number of patches. Intuitively, every patch can be treated as a software revision during regression testing; thus, traditional Regression Test Selection (RTS) techniques can be leveraged to only execute the tests affected by each patch (as the other tests would keep the same outcomes) to further reduce patch execution time. However, few APR systems actually adopt RTS and there is still a lack of systematic studies demonstrating the benefits of RTS and the impact of different RTS strategies on APR. To this end, this paper presents the first extensive study of widely-used RTS techniques at different levels (i.e., class/method/statement levels) for 12 state-of-the- art APR systems on over 2M patches. Our study reveals various practical guidelines for bridging the gap between APR and regression testing, including: (1) the number of patches widely used for measuring APR efficiency can incur skewed conclusions, and the use of inconsistent RTS configurations can further skew the conclusions; (2) all studied RTS techniques can substantially improve APR efficiency and should be considered in future APR work; (3) method- and statement-level RTS outperform class-level RTS substantially, and should be preferred; (4) RTS techniques can substantially outperform state-of-the-art test prioritization techniques for APR, and combining them can further improve APR efficiency; and (5) traditional Regression Test Prioritization (RTP) widely studied in regression testing performs even better than APR-specific test prioritization when combined with most RTS techniques. Furthermore, we also present the detailed impact of different patch categories and patch validation strategies on our findings. Furthermore, we also present the detailed impact of different patch categories and patch validation strategies on our findings."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124275"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Jun Yang"],"dc:subject":["Automated Program Repair","Regression Testing"],"dc:title":["When automated program repair meets regression testing – An extensive study on 2 million patches"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}