Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149074
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dc.contributor.authorLi, Yien_US
dc.contributor.authorZhu, Chenguangen_US
dc.contributor.authorGligoric, Milosen_US
dc.contributor.authorRubin, Juliaen_US
dc.contributor.authorChechik, Marshaen_US
dc.date.accessioned2021-05-21T09:24:46Z-
dc.date.available2021-05-21T09:24:46Z-
dc.date.issued2019-
dc.identifier.citationLi, Y., Zhu, C., Gligoric, M., Rubin, J. & Chechik, M. (2019). Precise semantic history slicing through dynamic delta refinement. Automated Software Engineering, 26(4), 757-793. https://dx.doi.org/10.1007/s10515-019-00260-8en_US
dc.identifier.issn0928-8910en_US
dc.identifier.other0000-0003-4562-8208-
dc.identifier.other0000-0001-7280-1614-
dc.identifier.other0000-0002-6301-3517-
dc.identifier.urihttps://hdl.handle.net/10356/149074-
dc.description.abstractSemantic history slicing solves the problem of extracting changes related to a particular high-level functionality from software version histories. State-of-the-art techniques combine static program analysis and dynamic execution tracing to infer an over-approximated set of changes that can preserve the functional behaviors captured by a test suite. However, due to the conservative nature of such techniques, the sliced histories may contain irrelevant changes. In this paper, we propose a divide-and-conquer-style partitioning approach enhanced by dynamic delta refinement to produce much smaller semantic history slices. We utilize deltas in dynamic invariants generated from successive test executions to learn significance of changes with respect to the target functionality. Additionally, we introduce a file-level commit splitting technique for untangling unrelated changes introduced in a single commit. Empirical results indicate that these measurements accurately rank changes according to their relevance to the desired test behaviors and thus partition history slices in an efficient and effective manner.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.language.isoenen_US
dc.relationMOE Tier1 2018-T1-002-069en_US
dc.relation.ispartofAutomated Software Engineeringen_US
dc.rights© 2019 Springer Science+Business Media. This is a post-peer-review, pre-copyedit version of an article published in Automated Software Engineering. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10515-019-00260-8en_US
dc.subjectEngineering::Computer science and engineering::Software::Software engineeringen_US
dc.titlePrecise semantic history slicing through dynamic delta refinementen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.organizationUniversity of Texas at Austinen_US
dc.contributor.organizationUniversity of British Columbiaen_US
dc.contributor.organizationUniversity of Torontoen_US
dc.identifier.doi10.1007/s10515-019-00260-8-
dc.description.versionAccepted versionen_US
dc.identifier.scopus2-s2.0-85067827047-
dc.identifier.issue4en_US
dc.identifier.volume26en_US
dc.identifier.spage757en_US
dc.identifier.epage793en_US
dc.subject.keywordsSemantic History Slicingen_US
dc.subject.keywordsProgram Analysisen_US
dc.description.acknowledgementThis research is partly supported by the Singapore Ministry of Education Academic Research Fund Tier 1 (award No. 2018-T1-002-069).en_US
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