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Chapter 5. Growth Dynamics
large and complex classes need not directly service a large number of
other classes.
5.4.2 Metric Data Distributions are not Normal
Software systems that we analysed contained many hundreds of classes.
But how are they distributed? Are they highly skewed, as found by other
researchers? When we analysed this data, we found that our observations
confirm findings from other researchers [21,55,223,270,299], in
that they do not fit a gaussian distributions. Further, we consistently
found positive values for skewness clearly indicating that in all cases
the distributions are skewed to contain a fat tail.
An example typical of the metric data in our data set is illustrated in Figure
5.1 and it shows the relative frequency distributions, for the metrics
Number of Methods and Fan-Out Count for release 2.5.3 of the Spring
framework (a popular Java/J2EE light-weight application container).
In both cases the distributions, are significantly skewed. However, the
shape of distribution is different. This is a pattern that is recurring
and common, that is, though the distributions are non-guassian and
positively skewed with fat tails, they are different for different systems
and metrics. A complete list of all descriptive statistics and the result
from our test for normality is presented in Appendix E.
5.4.3 Evolution of Metric Distributions
The upper and lower boundaries of the metric data distribution is bounded
within a fairly narrow range. Figure 5.5 presents the boundaries of the
histograms based on the minimum and maximum values of Number of
Branches, In-Degree Count, Number of Methods and Out-Degree Count
attained across all versions of the Spring Framework. The figures show
that relative frequency distributions of these measures have a distinct
profile that is bounded in a small range. The notable fact is that this
remarkable stability is observable over an evolution period of 5 years.
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