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Chapter 5. Growth Dynamics
in 9 out of the 10 metrics, the exception was In-Degree Count. In all
metrics except In-Degree Count, the range of ρ values was dispersed
between 0.98 and −0.95. This observation is shown in Figure 5.10 and
the range for In-Degree Count is clearly different from the rest of the
metrics under consideration.
Interestingly, we also observed in the other 9 metrics, the Gini coefficient
values trended up for some metrics, while others moved down in
the same system. There were also very strong positive and strong negative
trends for certain metrics, though not consistently similar across all
systems, for instance in PMD the ρ value for NOB was −0.98 while in Kolmafia
the value for NOB was +0.89. In approximately 30% of the cases,
there was no observable trend, with the ρ value in the range −0.35 to
+0.35 which shows that there is at best a weak relationship. Figure 5.10
provides a box plot that indicates the range and IRQ of correlation coefficients
for the 10 different metrics under study. As can be seen, the
range of values is very broad and in 5 metrics (ODC,PMC,FOC,LIC and
NOB) the median value was zero.
The In-Degree Count, a measure of popularity was the only one that
consistently showed a positive trend. In 20 systems the ρ value was
greater than 0.8 and in 11 systems the value was in the range 0.55 to 0.8.
3 systems showed a weak relationship (ρ values of 0.08, 0.22 and 0.33),
while only 3 systems (Struts, Tapestry and Ant consistently) showed a
strong negative trend (ρ < −0.6).
5.4.8 Summary of Observations
The aim of our study into the nature of growth in software was to understand
if evolution causes growth to be evenly distributed among the
various classes in the software system, or if a certain classes tend to
gain more complexity and size. In order to answer this question, we
studied how the metric data distribution changes over time.
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