thesis - Faculty of Information and Communication Technologies ...

Chapter 1. Introduction
compute at the level of a version allow us to identify classes that have
been added, removed, modified and deleted between versions. Class
level change measures allow us to detect the magnitude and frequency
of change an individual class has undergone over its lifetime within the
software system. We use the information collected to derive a set of
common statistical properties, and identify if certain properties within
a class cause them to be more change-prone.
1.3 Main Research Outcomes
In this thesis we address the problem of identifying, in successful software
systems, where and how maintenance effort tends to be devoted.
We show that maintenance effort is, in general, spent on addition of new
classes with a preference to base new code on top of a small set of class
that provide key services. Interestingly, these choices make the heavily
used classes change-prone as they are modified to meet the needs of the
new clients.
This thesis makes a number of significant contributions to the software
evolution body of knowledge:
Firstly, we investigated the validity of Lehman’s Laws of software evolution
related to growth and complexity within our data set, and found
consistent support for the applicability of the following laws: First law
Continuing Change, third law Self Regulation, fifth law Conservation of
Familiarity, and the sixth law Continuing Growth. However, our analysis
was not able to provide sufficient evidence to show support for the
other laws.
Secondly, we investigated how software metric data distributions (as
captured by a probability density function) change over time. We confirm
that software metric data exhibits highly skewed distributions, and
show that the use of first order statistical summary measures (such as
mean and standard deviation) is ineffective when working with such
data. We show that by using the Gini coefficient [91], a high-order
statistical measure widely used in the field of economics, we can inter-
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