thesis - Faculty of Information and Communication Technologies ...

Chapter 2. Software Evolution
nential growth in the number of plug-ins for the Eclipse platform [192]
is similar to that of the driver sub-system in Linux and shows that certain
architectural styles can allow the overall software systems to grow
at super-linear rates, suggesting limitations to Lehman’s laws.
Studies that found super-linear growth rates [101, 119, 120, 127, 153,
239] show that it is possible to manage the increase in volumetric complexity
and the consequent structural complexity. The implication of
these studies is that certain architectural choices made early in the
life cycle can have an impact on the growth rate, and a certain level of
structural complexity can be sustained without a corresponding investment
of development effort (in contrast to the expectations of the laws
of software evolution).
A consistent method that is generally applied by earlier studies of growth
has been to observe how certain system wide measures change over
time (for example, Number of modules). These observations have then
typically been interpreted within the context of Lehman’s laws of evolution
in order to understand growth dynamics within evolving software
systems. Though these previous studies have improved our understanding
of how software evolves, there is limited knowledge with
respect to how this growth is distributed among the various abstractions
of a software system.
An early study that has provided some data about the distribution of
growth is the one undertaken by Gall et al. [85] that suggests that different
modules grow at different rates. This observation is also confirmed
by Barry et al. [17]. Although these studies highlight that growth rates
can differ across modules, they do not discuss in depth how the growth
is distributed and what the impact of this distribution is on the overall
evolution of the software that they study. More recently, Israeli et
al. [127] investigated the Linux kernel and identified that average complexity
is decreasing. The interesting aspect of the study by Israeli et al.
was that they note that the reduction of the average complexity was a result
of developers adding more functions with lower relative complexity.
However, all of these studies have focused on individual systems and on
a small set of metrics, and hence there is a gap in our understanding
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