Programming complexity: Difference between revisions

Revision as of 16:38, 29 March 2008

Revision as of 16:44, 29 March 2008

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'''Programming Complexity''' is the [[complexity]] of [[computer program|program]]s, [[Computer programming|programming]] and [[programming languages|languages]], and one of the [[unsolved problems in software engineering]].

Applications are complex to the extantextent that when programmers [[Resignation|resign]] or are [[termination of employment|terminated]], [[company|companies]] [[fail]] if those companies have no one capable of [[understanding]] what the programmers did {{Who|date=December 2007}}. Because of this, [[researcher]]s establish [[software metric|metric]]s which [[measure]] the complexity and can be used to figure out how to reduce the complexity of the [[software]].

One measure of the complexity of a program is the complexity of the [[algorithm]], which is the number of steps to [[equation|solve]] an algorithm or [[problem]] (see [[optimization problem]]).<ref>[http://www.cs.bham.ac.uk/~mhe/foundations2/node121.html How to determine the complexity of an algorithm<!-- Bot generated title -->]</ref> A smaller complexity means less steps and a more [[Efficiency|efficient]] program. Efficiency and [[optimization]] are important to [[software engineer|professional writers of software]] who often [[Source code|code]] millions of interconnected [[Method (computer science)|method]]s in one program, all with large complexities and [[Analysis|analytic]] and [[Organizing|organization]]al [[difficulty]].

There are several metrics one can use to measure programming complexity:

* data complextiycomplexity (Chapin Metric)

* data flow complexity (Elshof Metric)

* data access complexity (Card Metric)

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* branching complexity (Sneed Metric)

* language complexity (Halstead Metric)

* [[Cyclomatic complexity|cyclomatic complexity]]