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Using slicing techniques to support scalable rigorous analysis of class models

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dc.contributor.authorSun, Wuliang, author
dc.contributor.authorRay, Indrakshi, advisor
dc.contributor.authorBieman, James M., committee member
dc.contributor.authorMalaiya, Yashwant K., committee member
dc.contributor.authorCooley, Daniel S., committee member
dc.date.accessioned2015-08-27T03:57:06Z
dc.date.available2015-08-27T03:57:06Z
dc.date.issued2015
dc.description.abstractSlicing is a reduction technique that has been applied to class models to support model comprehension, analysis, and other modeling activities. In particular, slicing techniques can be used to produce class model fragments that include only those elements needed to analyze semantic properties of interest. However, many of the existing class model slicing techniques do not take constraints (invariants and operation contracts) expressed in auxiliary constraint languages into consideration when producing model slices. Their applicability is thus limited to situations in which the determination of slices does not require information found in constraints. In this dissertation we describe our work on class model slicing techniques that take into consideration constraints expressed in the Object Constraint Language (OCL). The slicing techniques described in the dissertation can be used to produce model fragments that each consists of only the model elements needed to analyze specified properties. The slicing techniques are intended to enhance the scalability of class model analysis that involves (1) checking conformance between an object configuration and a class model with specified invariants and (2) analyzing sequences of operation invocations to uncover invariant violations. The slicing techniques are used to produce model fragments that can be analyzed separately. An evaluation we performed provides evidence that the proposed slicing techniques can significantly reduce the time to perform the analysis.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierSun_colostate_0053A_12895.pdf
dc.identifier.urihttp://hdl.handle.net/10217/166933
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectslicing
dc.subjectclass model
dc.subjectUML
dc.titleUsing slicing techniques to support scalable rigorous analysis of class models
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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