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dc.contributor.authorKing'ori, Ann W.
dc.contributor.authorMuketha, Geoffrey M.
dc.contributor.authorMicheni, E. M.
dc.date.accessioned2019-11-01T13:08:16Z
dc.date.available2019-11-01T13:08:16Z
dc.date.issued2019-07
dc.identifier.citationInternational Journal of Software Engineering & Applications (IJSEA), Vol.10, No.4, July 2019en_US
dc.identifier.urihttp://hdl.handle.net/123456789/4358
dc.identifier.urihttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3441634
dc.identifier.urihttps://www.researchgate.net/publication/335017042_A_Literature_Survey_of_Cognitive_Complexity_Metrics_for_Statechart_Diagrams
dc.identifier.urihttp://dx.doi.org/10.2139/ssrn.3441634
dc.description.abstractStatechart diagrams have inherent complexity which keeps increasing every time the diagrams are modified. This complexity poses problems in comprehending statechart diagrams. The study of cognitive complexity has over the years provided valuable information for the design of improved software systems. Researchers have proposed numerous metrics that have been used to measure and therefore control the complexity of software. However, there is inadequate literature related to cognitive complexity metrics that can apply to measure statechart diagrams. In this study, a literature survey of statechart diagrams is conducted to investigate if there are any gaps in the literature. Initially, a description of UML and statechart diagrams is presented, followed by the complexities associated with statechart diagrams and finally an analysis of existing cognitive complexity metrics and metrics related to statechart diagrams. Findings indicate that metrics that employ cognitive weights to measure statechart diagrams are lacking.en_US
dc.language.isoenen_US
dc.subjectUMLen_US
dc.subjectStatechart diagramsen_US
dc.subjectSoftware metricsen_US
dc.subjectCognitive complexity metricsen_US
dc.subjectstatechart complexity metricsen_US
dc.titleA Literature Survey of Cognitive Complexity Metrics for Statechart Diagramsen_US
dc.typeArticleen_US


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