dc.contributor.author | Field, S. | |
dc.contributor.author | Davey, N. | |
dc.contributor.author | Frank, R. | |
dc.date.accessioned | 2010-11-04T14:45:23Z | |
dc.date.available | 2010-11-04T14:45:23Z | |
dc.date.issued | 1995 | |
dc.identifier.citation | Field , 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.other | PURE: 96611 | |
dc.identifier.other | PURE UUID: 529d6a3e-db95-4227-9a96-62af6c681861 | |
dc.identifier.other | dspace: 2299/4960 | |
dc.identifier.uri | http://hdl.handle.net/2299/4960 | |
dc.description.abstract | Units 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.iso | eng | |
dc.publisher | University of Hertfordshire | |
dc.relation.ispartofseries | UH Computer Science Technical Report | |
dc.title | Using neural networks to analyse software complexity | en |
dc.contributor.institution | School of Computer Science | |
dc.contributor.institution | Science & Technology Research Institute | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |