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Chapter 2. Software Evolution
developers will create a release of a software system once it is deemed to
be relatively stable and defect-free [254]. By focusing on how a sequence
of releases of a software system evolve, the release-based studies gain
knowledge about the dynamics of change between stable releases and
more importantly have the potential to identify releases with significant
changes (compared to a previous release). The assumption that developers
formally release only a stable build allows release based studies
to identify patterns of evolution across multiple software systems (since
they compare what developers consider stable releases across different
systems) [254].
Change based studies, on the other hand, view evolution as the aggregate
outcome of a number of individual changes over the entire life
cycle [25]. That is, they primarily analyze information generated during
the development of a release. Due to the nature of information that they
focus on, change based studies tend to provide an insight into the process
of evolution that is comparatively more developer centric. Although
change based studies can also be used to determine changes from the
end-user perspective, additional information about releases that have
been deployed for customers to use has to be taken into consideration
during analysis.
Though software evolution can be studied from both a release based
as well as the change based perspective, most of the studies in the literature
have been based on an analysis of individual changes [139].
A recent survey paper by Kagdi et al. [139] reports on the result of
an investigation into the various approaches used for mining software
repositories in the context of software evolution. Kadgi et al. show that
most studies of evolution tend to rely on individual changes as recorded
in the logs generated and maintained by configuration/defect management
systems (60 out of the 80 papers that they studied). Though a
specific reason for the preference towards studying these change logs is
not provided in the literature, it is potentially because the logs permit
an analysis of software systems independent of the programming language,
and the data is easily accessible directly from the tools typically
used by the development team (e.g. CVS logs).
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