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dc.contributor.authorAntony, Elizabeth
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2014-03-03 19:36:25.776en
dc.date.accessioned2014-03-04T14:39:58Z
dc.date.available2014-03-04T14:39:58Z
dc.date.issued2014-03-04
dc.identifier.urihttp://hdl.handle.net/1974/8646
dc.descriptionThesis (Master, Computing) -- Queen's University, 2014-03-03 19:36:25.776en
dc.description.abstractIn this thesis, we present an approach for identifying near-miss interaction clones in reverse-engineered UML behavioural models. Our goal is to identify patterns of interaction ("conversations") that can be used to characterize and abstract the run-time behaviour of web applications and other interactive systems. In order to leverage robust near-miss code clone technology, our approach is text-based, working on the level of XMI, the standard interchange serialization for UML. Behavioural model clone detection presents several challenges - first, it is not clear how to break a continuous stream of interaction between lifelines (lifelines represent the objects or actors in the system) into meaningful conversational units. Second, unlike programming languages, the XMI text representation for UML is highly non-local, using attributes to reference information in the model file remotely. In this work we use a set of contextualizing source transformations on the XMI text representation to reveal the hidden hierarchical structure of the model and granularize behavioural interactions into conversational units. Then we adapt NiCad, a near-miss code clone detection tool, to help us identify conversational clones in reverse-engineered behavioural models. These conversational clones are then analysed to find worrisome patterns of security access violations.en_US
dc.languageenen
dc.language.isoenen_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectaccess violationsen_US
dc.subjectclone detection, behavioral modelsen_US
dc.titleAn Approach to Clone Detection in Behavioral Modelsen_US
dc.typeThesisen_US
dc.description.degreeMasteren
dc.contributor.supervisorCordy, James R.en
dc.contributor.supervisorAlalfi, Manar H.en
dc.contributor.departmentComputingen


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