Complexity is everywhere in the software life cycle: requirements, analysis, design, and of course, implementation. Complexity is usually an undesired property of software because complexity makes software harder to read and understand, and therefore harder to change; also, it is believed to be one cause of the presence of defects. Of all the artifacts produced in a software project, source code is the easiest option to measure complexity. However, several decades of software research have failed to produce a consensus about what metrics best reflect the complexity of a given piece of code. It’s hard even to compare two pieces of code written in different programming languages and say which code is more complex. Because of this lack of resolution, a myriad of possible metrics are currently offered to measure the complexity of a program. What does the research say are the best metrics for each particular case? Are all these metrics any better than very simple source code metrics, such as lines of code?

In this chapter, we take advantage of the huge amount of open source software available to study the relationships between different size and complexity metrics. To avoid suffocating in the myriads of attributes and metrics, we focus only on one programming language: C, a “classic” in software development that remains one of the most popular programming languages. We measure a grab-bag of metrics ranging from the simplest and most commonly cited (lines of code) to some rather sophisticated syntactic metrics for a set of about 300,000 files. From this we have found out which metrics are independent from a statistical point of view—that is to say, whether traditional complexity metrics actually provided more information than the simple lines-of-code approach.