thesis - Faculty of Information and Communication Technologies ...

Chapter 5. Growth Dynamics
Though, fitting distributions has been shown to have merit for modeling
networks [210] and to infer how these networks have been created,
software evolution is better modelled by analysing the evolution history
as we can reduce the number of assumptions one has to make. Rather
than attempting to infer the generative process from a single release of
a software system, we can gain more insight into the evolutionary pressures
by analysing the changing metric distribution over time. In our
work, we take this approach and study the metric distributions as they
change over time in order gain a better understanding of the underlying
evolutionary processes.
Though there has been progress over the last decade in this field, there
is still no widely-accepted distribution that captures consistently and
reliably software metric data. But more importantly, we are not required
to fit a given software metric to particular distributions in order
to interpret it. What is needed is a set of measures that reliably and consistently
summarize properties of the distribution allowing for effective
inferences to be made about the evolution of a software system.
5.2 Summarizing Software Metrics
5.2.1 Gini Coefficient - An Overview
Given the skewed nature of metric data we are in need of methods that
can effectively summarise this data and provide effective insight into
the current state of a software system as well as detect worthwhile
changes as the software evolves. In this section we introduce the Gini
Coefficient, a measure that is effective when dealing with metric data
and motivate its applicability for analysing evolving metric data distributions.
One of the key pieces of information we wish to obtain from software
metrics is the allocation of functionality within the system. Understanding
whether the system has a few classes that implement most
of the methods or whether methods are widely distributed gives us an
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