The Full Wiki

More info on Function point

Function point: Wikis


Note: Many of our articles have direct quotes from sources you can cite, within the Wikipedia article! This article doesn't yet, but we're working on it! See more info or our list of citable articles.


From Wikipedia, the free encyclopedia

A function point is a unit of measurement to express the amount of business functionality an information system provides to a user. Function points are the units of measure used by the IFPUG Functional Size Measurement Method. The IFPUG FSM Method is an ISO recognised software metric to size an information system based on the functionality that is perceived by the user of the information system, independent of the technology used to implement the information system. The IFPUG FSM Method (ISO/IEC 20926 SOFTWARE ENGINEERING - FUNCTION POINT COUNTING PRACTICES MANUAL) is one of five currently recognised ISO standards for Functionally sizing software.



Function points were defined in 1979 in A New Way of Looking at Tools by Allan Albrecht at IBM.[1] The functional user requirements of the software are identified and each one is categorized into one of five types: outputs, inquiries, inputs, internal files, and external interfaces. Once the function is identified and categorised into a type, it is then assessed for complexity and assigned a number of function points. Each of these functional user requirements maps to an end-user business function, such as a data entry for an Input or a user query for an Inquiry. This distinction is important because it tends to make the functions measured in function points map easily into user-oriented requirements, but it also tends to hide internal functions (e.g. algorithms), which also require resources to implement. Over the years there have been different approaches proposed to deal with this perceived weakness, however there is no ISO recognised FSM Method that includes algorithmic complexity in the sizing result. The variations of the Albrecht based IFPUG method designed to make up for this (and other weaknesses) include:

  • Early and easy function points. Adjusts for problem and data complexity with two questions that yield a somewhat subjective complexity measurement; simplifies measurement by eliminating the need to count data elements.
  • Engineering function points. Elements (variable names) and operators (e.g., arithmetic, equality/inequality, Boolean) are counted. This variation highlights computational function.[2] The intent is similar to that of the operator/operand-based Halstead Complexity Measures.
  • Bang measure - Defines a function metric based on twelve primitive (simple) counts that affect or show Bang, defined as "the measure of true function to be delivered as perceived by the user." Bang measure may be helpful in evaluating a software unit's value in terms of how much useful function it provides, although there is little evidence in the literature of such application. The use of Bang measure could apply when re-engineering (either complete or piecewise) is being considered, as discussed in Maintenance of Operational Systems—An Overview.
  • Feature points. Adds changes to improve applicability to systems with significant internal processing (e.g., operating systems, communications systems). This allows accounting for functions not readily perceivable by the user, but essential for proper operation.

See also


  1. ^ A. J. Albrecht, “Measuring Application Development Productivity,” Proceedings of the Joint SHARE, GUIDE, and IBM Application Development Symposium, Monterey, California, October 14–17, IBM Corporation (1979), pp. 83–92.
  2. ^ Engineering Function Points and Tracking System, Software Technology Support Center, Retrieved on May 14, 2008

External links



Got something to say? Make a comment.
Your name
Your email address