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Chapter 5. Growth Dynamics
can change significantly as the software system evolves as can be seen
with the Number of Fields (NOF) metric in Proguard (see Figure 5.11).
Moreover, we found repeatedly that the Gini coefficients for metrics that
may be considered correlated, diverge independently of the underlying
metric data relationship. Though Load Instruction Count and Store Instruction
Count are strongly correlated (often well over 0.90), we need
to analyze summary measures derived from them separately also. We
observed in some instances that the Load Instruction Count Gini Coefficients
changed independently of the Store Instruction Count Gini Coefficient
values. For example, between the release 3.0 and 3.1 of Check-
Style the Load Instruction Count Gini Coefficient changes from 0.870 to
0.804 while Store Instruction Count Gini Coefficient only changes from
0.831 to 0.827.
Interestingly, our findings contradict the recommendation provided in
earlier work by other researchers [40, 41, 110, 120] who observed a
strong correlation between various size and complexity metrics and
hence argued that a smaller sub-set of the measures is adequate. However,
the earlier observations did not consider how the strength of the
correlation changes over time. We however find that it is important to
consider multiple dimensions since the strength of the correlation cannot
be considered to be a general property and it is clearly influenced
during the maintenance of a software system. Furthermore, an abnormal
change in the correlation between various metrics can be used as
a trigger for further investigation.
5.5.2 Dynamics of Growth
How is maintenance effort focused by developers? When developers
create a new version, they build it on top of existing code by adding new
classes, modifying and potentially removing some existing classes. If the
tendency of developers is to grow the size and complexity of a system
by extending existing classes, then over time this preference causes
older classes to gain size and complexity. This preferential modification
will be observable as an upward trend in the Gini Coefficient values
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