Graph drawing strategies for large UML State Machine diagrams

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dc.contributor.authorContreras Franco, Juan Pablo
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T14:38:06Z
dc.date.available2019-07-03T14:38:06Z
dc.date.issued2017
dc.description.abstractAs systems grow in complexity, their development and maintainability cost increase since there is a cognitive effort involved in the process of understanding their state and the relationships of their parts. This report describes how two graph drawing strategies can improve the depictions of UML state machines from a particular business case. The intention is to show new options to improve the readability and overall quality of the outcome produced by an in-house graph drawing solution. This project address the features of the problem that are concerned about the graph quality of the software modeling tools in use. These features relate to how the user perceives the state machine drawings. An implementation of a proof of concept is the base to explore an alternative graph drawing framework with the purpose of motivating a discussion about the feasibility of migrating the current graph drawing engine into a new one. The work concludes that it is possible to customize an existing framework to fulfill the usability standards for UML state machine layouts. Further improvements on the proof of concept are required. Mainly, the geometric information must get involved in realistic scenarios.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/252095
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titleGraph drawing strategies for large UML State Machine diagrams
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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