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        Using neural networks to analyse software complexity

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        CSTR 217.pdf (PDF, 1Mb)
        Author
        Field, S.
        Davey, N.
        Frank, R.
        Attention
        2299/4960
        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.
        Publication date
        1995
        Other links
        http://hdl.handle.net/2299/4960
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