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dc.contributor.authorField, S.
dc.contributor.authorDavey, N.
dc.contributor.authorFrank, R.
dc.date.accessioned2010-11-04T14:45:23Z
dc.date.available2010-11-04T14:45:23Z
dc.date.issued1995
dc.identifier.citationField , S , Davey , N & Frank , R 1995 , Using neural networks to analyse software complexity . UH Computer Science Technical Report , vol. 217 , University of Hertfordshire .
dc.identifier.otherPURE: 96611
dc.identifier.otherPURE UUID: 529d6a3e-db95-4227-9a96-62af6c681861
dc.identifier.otherdspace: 2299/4960
dc.identifier.urihttp://hdl.handle.net/2299/4960
dc.description.abstractUnits of software are represented as points in a multidimensional space, by calculating 12 measures of software complexity for each unit. To large sets of commercial software are thereby represented as 2236 and 4456 12-ary vectors respectively. These two sets of vectors are then clustered by a variety of competitive neural networks. It is found that the software does not fall into any simple set of clusters, but that a complex pattern of clustering emerges. These clusters give a view of the structural similarity of units of code in the data sets.en
dc.language.isoeng
dc.publisherUniversity of Hertfordshire
dc.relation.ispartofseriesUH Computer Science Technical Report
dc.titleUsing neural networks to analyse software complexityen
dc.contributor.institutionSchool of Computer Science
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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