Using neural networks to analyse software complexity

S. Field, N. Davey, R. Frank

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    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.
    Original languageEnglish
    PublisherUniversity of Hertfordshire
    Publication statusPublished - 1995

    Publication series

    NameUH Computer Science Technical Report
    PublisherUniversity of Hertfordshire
    Volume217

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