{"id":{"repo_id":"toronto-retro","oai_identifier":"oai:utoronto.scholaris.ca:1807/16740"},"canonical_url":"https://search.dev.ndltd.org/etd/toronto-retro/oai:utoronto.scholaris.ca:1807/16740","repository":{"repo_id":"toronto-retro","name":"University of Toronto","base_url":"https://utoronto.scholaris.ca/server/oai/request"},"display":{"title":"Behavioural Model Fusion","abstract":"In large-scale model-based development, developers periodically need to combine collections of interrelated models. These models may capture different features of a system, describe alternative perspectives on a single feature, or express ways in which different features alter one another's structure or behaviour. We refer to the process of combining a set of interrelated models as \"model fusion\". A number of factors make model fusion complicated. Models may overlap, in that they refer to the same concepts, but these concepts may be presented differently in each model, and the models may contradict one another. Models may describe independent system components, but the components may interact, potentially causing undesirable side effects. Finally, models may cross-cut, modifying one another in ways that violate their syntactic or semantic properties. In this thesis, we study three instances of the fusion problem for \"behavioural models\", motivated by real-world applications. The first problem is combining \"partial\" models of a single feature with the goal of creating a more complete description of that feature. The second problem is maintenance of \"variant\" specifications of individual features. The goal here is to combine the variants while preserving their points of difference (i.e., variabilities). The third problem is analysis of interactions between models describing \"different\" features. Specifically, given a set of features, the goal is to construct a composition such that undesirable interactions are absent. We provide an automated tool-supported solution to each of these problems and evaluate our solutions. The main novelties of the techniques presented in this thesis are (1) preservation of semantics during the fusion process, and (2) applicability to large and evolving collections of models. These are made possible by explicit modelling of partiality, variability and regularity in behavioural models, and providing semantic-preserving notions for relating these models.","abstract_html":"In large-scale model-based development, developers periodically need to combine collections of interrelated models. These models may capture different features of a system, describe alternative perspectives on a single feature, or express ways in which different features alter one another's structure or behaviour. We refer to the process of combining a set of interrelated models as "model fusion". A number of factors make model fusion complicated. Models may overlap, in that they refer to the same concepts, but these concepts may be presented differently in each model, and the models may contradict one another. Models may describe independent system components, but the components may interact, potentially causing undesirable side effects. Finally, models may cross-cut, modifying one another in ways that violate their syntactic or semantic properties. In this thesis, we study three instances of the fusion problem for "behavioural models", motivated by real-world applications. The first problem is combining "partial" models of a single feature with the goal of creating a more complete description of that feature. The second problem is maintenance of "variant" specifications of individual features. The goal here is to combine the variants while preserving their points of difference (i.e., variabilities). The third problem is analysis of interactions between models describing "different" features. Specifically, given a set of features, the goal is to construct a composition such that undesirable interactions are absent. We provide an automated tool-supported solution to each of these problems and evaluate our solutions. The main novelties of the techniques presented in this thesis are (1) preservation of semantics during the fusion process, and (2) applicability to large and evolving collections of models. These are made possible by explicit modelling of partiality, variability and regularity in behavioural models, and providing semantic-preserving notions for relating these models.","abstract_has_math":false,"creators":["Nejati, Shiva"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Computer Science","school":null,"contributors":[],"advisors":["Chechik, Marsha"],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-01-19T20:30:18Z","date_published":"2009-01-19T20:30:18Z","updated_at":"2026-07-27T21:27:54Z","subjects":["Model Merging","Model Composition","Model Management","Behavioural Models","Refinement","Temporal Logic","Bisimulation/Simulation","State Machine"],"languages":["en_ca"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1807/16740","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Chechik, Marsha"]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science"]},{"key":"dc:creator","label":"Author","values":["Nejati, Shiva"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2008-11"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2009-01-19T20:30:18Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["NO_RESTRICTION","2009-01-19T20:30:18Z"]},{"key":"dc:date.issued","label":"Date","values":["2009-01-19T20:30:18Z"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Model Merging","Model Composition","Model Management","Behavioural Models","Refinement","Temporal Logic","Bisimulation/Simulation","State Machine"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_ca"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1807/16740"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In large-scale model-based development, developers periodically need to combine collections of interrelated models. These models may capture different features of a system, describe alternative perspectives on a single feature, or express ways in which different features alter one another's structure or behaviour. We refer to the process of combining a set of interrelated models as \"model fusion\". A number of factors make model fusion complicated. Models may overlap, in that they refer to the same concepts, but these concepts may be presented differently in each model, and the models may contradict one another. Models may describe independent system components, but the components may interact, potentially causing undesirable side effects. Finally, models may cross-cut, modifying one another in ways that violate their syntactic or semantic properties. In this thesis, we study three instances of the fusion problem for \"behavioural models\", motivated by real-world applications. The first problem is combining \"partial\" models of a single feature with the goal of creating a more complete description of that feature. The second problem is maintenance of \"variant\" specifications of individual features. The goal here is to combine the variants while preserving their points of difference (i.e., variabilities). The third problem is analysis of interactions between models describing \"different\" features. Specifically, given a set of features, the goal is to construct a composition such that undesirable interactions are absent. We provide an automated tool-supported solution to each of these problems and evaluate our solutions. The main novelties of the techniques presented in this thesis are (1) preservation of semantics during the fusion process, and (2) applicability to large and evolving collections of models. These are made possible by explicit modelling of partiality, variability and regularity in behavioural models, and providing semantic-preserving notions for relating these models."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["PhD"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Behavioural Model Fusion"]}]}],"canonical_facts":{"dc:contributor.advisor":["Chechik, Marsha"],"dc:contributor.department":["Computer Science"],"dc:creator":["Nejati, Shiva"],"dc:date":["2008-11"],"dc:date.accessioned":["2009-01-19T20:30:18Z"],"dc:date.available":["NO_RESTRICTION","2009-01-19T20:30:18Z"],"dc:date.issued":["2009-01-19T20:30:18Z"],"dc:description.abstract":["In large-scale model-based development, developers periodically need to combine collections of interrelated models. 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The first problem is combining \"partial\" models of a single feature with the goal of creating a more complete description of that feature. The second problem is maintenance of \"variant\" specifications of individual features. The goal here is to combine the variants while preserving their points of difference (i.e., variabilities). The third problem is analysis of interactions between models describing \"different\" features. Specifically, given a set of features, the goal is to construct a composition such that undesirable interactions are absent. We provide an automated tool-supported solution to each of these problems and evaluate our solutions. The main novelties of the techniques presented in this thesis are (1) preservation of semantics during the fusion process, and (2) applicability to large and evolving collections of models. 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