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Chapter 2. Software Evolution
used to guide development teams to identify abnormal growth patterns.
Further, we can also verify if developers follow what are considered good
software engineering practice and avoid god classes [237] as software
evolves. This form of analysis is also helpful in early identification of
abnormal and unusual changes. This is necessary because research
into software reliability shows us that structurally complex parts of the
software tend to contain a higher rate of defects [20,205,281,317] and
early identification of parts that are gaining in complexity will allow us
to take corrective actions sooner.
In our study we aim to close these gap in our understanding and observe
evolution from a different perspective. Rather than use global
size growth models to infer support for laws and understand the nature
of evolution, we study software evolution by observing how the
distribution of size and complexity changes over time. More specifically,
we undertake a longitudinal study that constructs probability density
functions of different metrics collected from the software systems, and
builds a descriptive model of evolution by observing how these metric
distributions change over time.
2.5 Studies of Change
It is a widely accepted that software systems that are in active use need
to be changed at some or many stages of its life in order to stay useful
[175, 188, 192]. Given that change is the one inherent constant of
an active and used software system, the key to a successful software
evolution approach lies, however, not only in adapting a system to support
the new requirements [87], but also in understanding the nature
and the dynamics of change. Managing and reducing the costs and
risks of changes to software that arise as part of the maintenance process
are important goals for both research and the practice of software
engineering [25, 220].
The laws of software evolution, as well as a number of other studies,
have consistently suggested that evolving software will grow and undergo
change as it is adapted [175,188,192,293]. Since software growth
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