thesis - Faculty of Information and Communication Technologies ...

Chapter 2. Software Evolution
A limitation of relying on individual changes is that the change log data
needs to be carefully processed [86] in order to identify if the changes
recorded are related to aspects of software system under study (for instance,
source code), and also to ensure that the changes are significant
(for example, minor edits in the code comments may need to be
eliminated if the emphasis of a study is to understand how developers
adapt the actual functional source code as they evolve the system).
Another constraint that studies relying of change logs face is raised by
Chen et al. [44] who found that developers in some open source projects
did not properly record all of the changes. In their study, Chen et al.
highlight that in two out of the three systems studied, over 60% of the
changes were not recorded, and as a consequence, the information provided
in the change logs cannot be considered to be representative of all
the changes that take place within a software system. The significant
drawback of change based studies is their heavy reliance on developers
providing consistent and regular information about individual changes.
There is currently no evidence that shows that developers record individual
changes carefully. Furthermore, the definition of an individual
change is likely to vary from developer to developer, as well as from
project to project.
In our study, we focus on how software evolves post-release both in
terms of growth and changes between the releases that developers have
made available to end-users. We focus on releases because an understanding
of evolution from this perspective is of greater value to
managers and developers as any post-release change, in general, has
a greater impact on the end users [220]. Furthermore, existing release
based studies have mainly investigated very few software systems
(typically less then 20), including the seminal work by Lehman [174]
which investigated only one large software system. The restriction on
small data sets was potentially unavoidable in earlier work [85,148,284]
due to the reliance on commercial software systems which have legal
restrictions that make it challenging to investigate, and to replicate
the experiments. The wide-spread and increasing availability of
open source software systems over the past decade has allowed researchers
to study distinct releases of a larger number of software systems
in order to understand evolution. However, even these studies
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