thesis - Faculty of Information and Communication Technologies ...

Abstract
In this thesis we address the problem of identifying where, in successful
software systems, maintenance effort tends to be devoted. By examining
a larger data set of open source systems we show that maintenance
effort is, in general, spent on addition of new classes. Interestingly, efforts
to base new code on stable classes will make those classes less
stable as they need to be modified to meet the needs of the new clients.
This thesis advances the state of the art in terms of our understanding
of how evolving software systems grow and change. We propose an
innovative method to better understand growth dynamics in evolving
software systems. Rather than relying on the commonly used method
of analysing aggregate system size growth over time, we analyze how
the probability distribution of a range of software metrics change over
time. Using this approach we find that the process of evolution typically
drives the popular classes within a software system to gain additional
clients over time and the increase in popularity makes these classes
change-prone.
Furthermore, we show that once a set of classes have been released,
they resist change and the modifications that they do undergo are in
general, small adaptations rather than substantive rework. The methods
we developed to analyze evolution can be used to detect releases
with systemic and architectural changes as well as identify presence of
machine generated code.
Finally, we also extend the body of knowledge with respect to validation
of the Laws of Software Evolution as postulated by Lehman. We find
consistent support for the applicability of the following laws of software
evolution: first law Continuing Change, third law Self Regulation, fifth
law Conservation of Familiarity, and the sixth law Continuing Growth.
However, our analysis was unable to find evidence to support the other
laws.
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