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Chapter 2. Software Evolution
The observation of segmented growth has been used to suggest that
developers periodically restructure and reorganise the code base potentially
causing a temporary reduction in size and complexity followed
by a period of renewed growth [175]. An example of this segmented
growth has been captured by Capiluppi et al. [38–41] in a sequence of
studies. They presented evidence that shows that open source software
systems tend to have segmented growth where each segment may have
a different growth rate. For instance, Capiluppi et al. note that Gaim
(an internet chat software that was investigated in their study) grows at
a super-linear rate early in its life cycle, with a large gap in development
followed by a linear growth rate.
The segmented growth pattern has also been confirmed by Smith et
al. [256] and by Wu et al. [304,305]. Smith et al. [256] studied 25 open
source systems developed in C/C++ and showed that growth rates are
not consistent during the evolution of a software system and that they
can change. More recently, Wu et al. [304,305] presented evidence of a
punctuated growth in open source software system based on a study of
3 systems (including Linux). Wu et al. observed that developers work
in periodic bursts of activity, where intensive effort goes into creating a
major release followed by a less active period where minor defects are
corrected. Additionally, work done by Hsi et al. [123] has also shown
how the evolutionary drivers result in asymmetric and clumpy growth.
Summary
Studies of software evolution that have investigated the phenomenon
of growth have shown that the rate of growth can be super-linear, linear
or sub-linear. Furthermore, since this growth has been shown to
be segmented, there are limitations in the value offered by an understanding
of the overall growth rate. Additionally, the lack of consistency
with respect to the observed growth rate in the studies of evolution
[101,127,152,153,192,217,239] shows that there are limitations
within the explanation of the dynamics as postulated by the laws of
software evolution [175]. Specifically, there is evidence to suggest that
the complexity that arises due to evolution does not necessarily create
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