Function point

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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 an ISO recognized 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.

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[edit] Introduction

Function points were defined in 1979 in A New Way of Looking at Tools by Alan Albrecht at IBM.[1] Basic function points are categorized into five groups: outputs, inquiries, inputs, files, and interfaces. A function point is defined as one end-user business function, such as a query for an input. This distinction is important because it tends to make a function point map easily into user-oriented requirements, but it also tends to hide internal functions, which also require resources to implement. To make up for this (and other weaknesses), some refinements to and/or variations of the basic Albrecht definition have been devised, including:

  • 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 measures (see 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."[3] 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 reengineering (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.

[edit] Function point analysis

The method of measuring the size of an information system and expressing it in a number of function points is called function point analysis (FPA). The method is kept up to date by worldwide cooperating FPA user groups like NESMA and IFPUG. Function point analysis expresses the functional size of an information system in a number of function points (for example: the size of a system is 314 FPs). The functional size may be used to:

  • budget application development or enhancement costs.
  • budget the annual maintenance costs of the application portfolio.
  • determine project productivity after completion of the project.
  • determine the Software Size for cost estimating.

FPA can also be used to find the testing effort required in the information system; The formula is Number of Test Cases = (Function Points)1.2

Function Points measures systems from a functional perspective they are independent of technology. Regardless of language, development method, or hardware platform used, the number of function points for a system will remain constant. The only variable is the amount of effort needed to deliver a given set of function points.

The Five Components of Function Points are:

  1. Data Functions → Internal Logical Files
  2. Data Functions → External Interface Files
  3. Transaction Functions → External Inputs
  4. Transaction Functions → External Outputs
  5. Transaction Functions → External Inquiries

[edit] Criticisms of Function Points

Function points, and many other software metrics, have been criticized as adding little value relative to the cost and complexity of the effort.[4][5] The effort in computing function points has only a marginal error reduction, in part, because much of the variance in software cost estimates are not considered (such as business changes, scope changes, unplanned resource constraints or reprioritizations, etc.). Also, if the measurement is used for the decision of whether to invest in the software, then a given measurement effort, it is argued, is more valuable if it is applied to measure benefits than costs. Applied information economics, which computes the economic value of such measures, often leads users to spend measurement efforts on other issues.

Some technical criticisms are indicated in Change points: A proposal for software productivity measurement, Journal of Systems Software, Vol. 31, September 1995, by Vernon V. Chatman III.

[edit] References

  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
  3. ^ Function Point Analysis, Carnegie Mellon Software Engineering Institute, Retrieved on May 14, 2008
  4. ^ Douglas Hubbard The IT Measurement Inversion, CIO Magazine, 1999
  5. ^ Douglas Hubbard How to Measure Anything: Finding the Value of Intangibles in Business, John Wiley & Sons, 2007

[edit] See also

[edit] External links