Programming complexity (or software complexity) is a term that includes software properties that affect internal interactions. Several commentators distinguish between the terms "complex" and "complicated". Complicated implies being difficult to understand, but ultimately knowable. Complex, by contrast, describes the interactions between entities. As the number of entities increases, the number of interactions between them increases exponentially, making it impossible to know and understand them all. Similarly, higher levels of complexity in software increase the risk of unintentionally interfering with interactions, thus increasing the risk of introducing defects when changing the software. In more extreme cases, it can make modifying the software virtually impossible. The idea of linking software complexity to software maintainability has been explored extensively by Professor Manny Lehman, who developed his Laws of Software Evolution. He and his co-author Les Belady explored numerous software metrics that could be used to measure the state of software, eventually concluding that the only practical solution is to use deterministic complexity models.

Types

The complexity of an existing program determines the complexity of changing the program. Problem complexity can be divided into two categories:

Measures

Several measures of software complexity have been proposed. Many of these, although yielding a good representation of complexity, do not lend themselves to easy measurement. Some of the more commonly used metrics are Several other metrics can be used to measure programming complexity: Tesler's Law is an adage in human–computer interaction stating that every application has an inherent amount of complexity that cannot be removed or hidden.

Chidamber and Kemerer Metrics

Chidamber and Kemerer proposed a set of programing complexity metrics widely used in measurements and academic articles: weighted methods per class, coupling between object classes, response for a class, number of children, depth of inheritance tree, and lack of cohesion of methods, described below: