Quite often we hear people talking about how to write better code, how to build systems better or how to leverage new technologies to produce better software. But how can we objectively quantify whether code is good or if it needs improving? How can we analyze an existing codebase to see what parts need improvement without spending weeks reading through the source code? The answer is provided by static analysis tools like pDepend and PHPLOC. But the results that they produce can be extremely cryptic and hard to understand. From acronyms like NPATH and CRAP to metrics like Cyclomatic Complexity and Fanout, there's a whole treasure trove of information just waiting to be deciphered.

In this talk, we'll explore some of the tools available for measuring software quality. We'll dive into some of the theory behind the metrics that they analyze while looking at some real world applications of those metrics. We'll also explore how to use these tools to gain valuable insight into legacy codebases. Finally, we'll look into methods to integrate those metrics into your development workflow to help improve the quality of the applications that you build.