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dc.contributor.authorKagdi, Huzefa Hatimbhai
dc.contributor.authorGethers, Malcom
dc.contributor.authorPoshyvanyk, Denys
dc.date.accessioned2013-08-22T19:47:32Z
dc.date.available2013-08-22T19:47:32Z
dc.date.issued2013-10
dc.identifier.citationKagdi, Huzefa Hatimbhai; Gethers, Malcom; Poshyvanyk, Denys. 2013. Integrating conceptual and logical couplings for change impact analysis in software. Empirical Software Engineering October 2013, v.18:no.5:pp 933-969en_US
dc.identifier.issn1382-3256
dc.identifier.otherWOS:000322462000004
dc.identifier.urihttp://dx.doi.org/10.1007/s10664-012-9233-9
dc.identifier.urihttp://hdl.handle.net/10057/6414
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractThe paper presents an approach that combines conceptual and evolutionary techniques to support change impact analysis in source code. Conceptual couplings capture the extent to which domain concepts and software artifacts are related to each other. This information is derived using Information Retrieval based analysis of textual software artifacts that are found in a single version of software (e.g., comments and identifiers in a single snapshot of source code). Evolutionary couplings capture the extent to which software artifacts were co-changed. This information is derived from analyzing patterns, relationships, and relevant information of source code changes mined from multiple versions in software repositories. The premise is that such combined methods provide improvements to the accuracy of impact sets compared to the two individual approaches. A rigorous empirical assessment on the changes of the open source systems Apache httpd, ArgoUML, iBatis, KOffice, and jEdit is also reported. The impact sets are evaluated at the file and method levels of granularity for all the software systems considered in the empirical evaluation. The results show that a combination of conceptual and evolutionary techniques, across several cut-off points and periods of history, provides statistically significant improvements in accuracy over either of the two techniques used independently. Improvements in F-measure values of up to 14% (from 3% to 17%) over the conceptual technique in ArgoUML at the method granularity, and up to 21% over the evolutionary technique in iBatis (from 9% to 30%) at the file granularity were reported.en_US
dc.description.sponsorshipUnited States NSF CCF-1016868, NSF CCF-0916260, NSF CCF-1156401, and NSF CCF-1218129 grants.en_US
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.relation.ispartofseriesEmpirical Software Engineering;v.18:no.5
dc.subjectChange impact analysisen_US
dc.subjectInformation Retrievalen_US
dc.subjectConceptual and logical couplingen_US
dc.subjectMining software repositoriesen_US
dc.subjectOpen-source softwareen_US
dc.subjectSoftware evolution and maintenanceen_US
dc.titleIntegrating conceptual and logical couplings for change impact analysis in softwareen_US
dc.typeArticleen_US
dc.description.versionPeer reviewed
dc.rights.holderCopyright © 2012, Springer Science+Business Media New York


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