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Chapter 4. Measuring Evolving Software
since it takes usually more effort to create a larger-size system than a
smaller one [78]. Examples of size metrics within the context of objectoriented
systems are the Number of Classes and the Number of Public
Methods within a class.
4.2.2 Complexity Metrics
Unlike size, complexity in software is an aspect that is harder to rigidly
define and is an aspect that is often perceived subjectively making it
harder to measure [116]. However, a number of researchers have put
forward metrics that capture complexity in software. Before, we outline
our approach to measuring complexity, we briefly explain why complexity
is hard to measure and the various attributes that need to be
considered when interpreting any measure of complexity.
What is complexity?
The Oxford dictionary defines complexity as “the state or quality of being
intricate or complicated” [69]. From a general perspective, a system that
is composed of many interacting parts whose behaviour or structure is
difficult to understand is frequently described to be complex. Modern
software systems are complex as they tend to have a large number of
interacting parts, makeing it difficult to properly understand the overall
behaviour, even when complete information about its components and
their inter-relations is available.
Some of the key contributors to complexity are [88, 222]:
1. The size of the system: more parts require a need to organise them
in order to properly comprehend,
2. The amount and depth of knowledge available (and used) to digest
and understand the system,
3. The level of abstraction that is possible, without loosing too much
information
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