Programming Complexity

Programming Complexity, which is often also referred to as Software Complexity is a term that encompasses numerous properties of a piece of software, all of which affect internal interactions. According to several commentators, including [http://www.astroprojects.com/morespace/johnnie/ Jonnie Moore] , 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 [MM Lehmam LA Belady; Program Evolution - Processes of Software Change 1985] , 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.

Many measures of software complexity have been proposed, many of these although giving a good representation of complexity, do not lend themselves to easy measurement. Some of the more commonly used metrics are

* McCabes cyclomatic complexity metric

* Elements of Software Science published in 1976 by Maurice Halstead and often referred to as Halstead's software science metrics. He introduced a family of metrics, for program length, programming effort and to estimate the intelligence content. All of them are generated from analysis of the combinations of the unique and total operators and operands of the program.

* Henry and Kafura introduced Software Structure Metrics Based on Information Flow in 1981 [Henry, S.; Kafura, D. IEEE Transactions on Software Engineering Volume SE-7, Issue 5, Sept. 1981 Page(s): 510 - 518] which measures complexity as a function of fan in and fan out. They define fan-in of a procedure as the number of local flows into that procedure plus the number of data structures from which that procedure retrieves information. Fan-out is defined as the number of local flows out of that procedure plus the number of data structures that the procedure updates. Local flows relate to data passed to and from procedures that call or are called by, the procedure in question. Henry and Kafura?s complexity value is defined as "the square of procedure length multiplied by fan-in multiplied by fan-out."

* A Metrics Suite for Object Oriented Design [Chidamber, S.R.; Kemerer, C.F. IEEE Transactions on Software Engineering Volume 20, Issue 6, Jun 1994 Page(s):476 - 493] was introduced by Chidamber and Kemerer in 1994 focusing, as the title suggests on metrics specifically for object oriented code. They introduce six OO complexity metrics; 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

There are several other metrics that can use to measure programming complexity:
* data complexity (Chapin Metric)
* data flow complexity (Elshof Metric)
* data access complexity (Card Metric)
* decisional complexity (McClure Metric)
* branching complexity (Sneed Metric)

References

ee also

*Software crisis (and subsequent programming paradigm solutions)
*Software metrics - quantitative measure of some property of a program.