Programming complexity

Programming complexity (or software complexity) is a term that includes many properties of a piece of software, all of which affect internal interactions. According to several commentators, there is a distinction between the terms complex and complicated. Complicated implies being difficult to understand but with time and effort, ultimately knowable. Complex, on the other hand, describes the interactions between a number of entities. As the number of entities increases, the number of interactions between them would increase exponentially, and it would get to a point where it would be impossible to know and understand all of them. Similarly, higher levels of complexity in software increase the risk of unintentionally interfering with interactions and so increases the chance of introducing defects when making changes. In more extreme cases, it can make modifying the software virtually impossible. The idea of linking software complexity to the maintainability of the software has been explored extensively by Professor Manny Lehman, who developed his Laws of Software Evolution from his research. He and his co-Author Les Belady explored numerous possible Software Metrics in their oft-cited book, that could be used to measure the state of the software, eventually reaching the conclusion that the only practical solution would be to use one that uses deterministic complexity models.
Measures.
Many 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
There are several other metrics that 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.
Types.
Associated with, and dependent on the complexity of an existing program, is the complexity associated with changing the program. The complexity of a problem can be divided into two parts:
Chidamber and Kemerer Metrics.
Chidamber and Kemerer proposed a set of programing complexity metrics, widely used in many measurements and academic articles. They are WMC, CBO, RFC, NOC, DIT, and LCOM, described below: