Abstract
A multivariate statistical procedure called multidimensional scaling is used to study the relationship of various software complexity metrics and program modules. The program modules that make up a software system are analysed and their effects towards the overall characteristics of a software are viewed. This multidimensional scaling technique is applied to a sample data set. The scaling procedure clustered the similar and dissimilar software complexity metrics. Program modules with low complexity and few errors clustered together, while modules which were complex were isolated. This technique shows promise in the identification of complex modules that potentially contain disproportionate errors prior to the testing phase. The ability of the scaling techniques to cluster similar and dissimilar characteristics is explained and graphically presented.
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