From Code Complexity Metrics to Program Comprehension

Code is hardly ever developed from scratch. Rather, new code typically needs to integrate with existing code and is dependent upon existing libraries. Two recent studies found that developers spend, on average, 58% and 70% of their time trying to comprehend code but only 5% of their time editing it.32,51 This implies that reading and understanding code is very important, both as an enabler of development and as a major cost factor during development. But as anyone who tries to read code can attest, it is hard to understand code written by others. This is commonly attributed, at least in part, to the code's complexity: the more complex the code, the harder it is to understand, and by implication, to work with. Identifying and dealing with complexity is considered important because the code's complexity may slow down developers and may even cause them to misunderstand it--possibly leading to programming errors. Conversely, simplicity is often extolled as vital for code quality. To gain a sound understanding of code complexity and its consequences, we must operationalize this concept. This means we need to devise ways to characterize it, ideally in a quantitative manner. And indeed, many metrics have been suggested for code complexity. Such metrics can then be used for either of two purposes. In industry, metrics are used to make predictions regarding code quality and development effort.