Function Point Metric(FPM)

This metric overcomes the shortcoming of the LOC metric

Why we use it? Because it can be used to easily estimate the size of the software product directly from the problem specification. The conceptual Idea behind the FPM is that size of the software product is directly dependent on the number of different functions or features it supports.

Steps to compute function point:

• When function is invoked, read some input data and transform it to corresponding output data.

Example the issue book feature of a library automation software takes the name of the book as input and displays its location and the number of copies available.

• Beside using the number of the input and output data values function point metric computes the size of a software product(in unit of function points or FPs.)

Function point is computed in two steps:

1) computing the unadjusted function point(UFP): UFP is refined to reflect the differences in the complexity of the different parameters.

1) number of inputs: In this is data item input by the user is counted. Data import should be distinguished from the inquiries. Individual data items input by the user are not considered in the calculation of the number of input, but a group of related inputs are considered as single input. Example:

2) Number of outputs: it refers to- reports printed, screen outputs, error messages produced While outputting the number of output individual data items with in a reports are not considered, but a set of related data items is counted as one input.

3) number of inquiries: Distinct interactive queries which can be made by the users. This inquiries are the user commands which require specified action by the system.

4) Number of files: Each logical file is counted.

5) Number of interface: Interfaces used to exchange information with other systems.

Once the unadjusted function point is computed, the technical complexity factor(TCF) is computed next.

TCF refines the UFP measured by considering 14 other factors such as high transaction rate, throughput, and response time requirement etc.

Each of these 14 factor is assigned from 0(not present or no influence) to 6 (strong influence). The resulting number are summed, yielding the total degree of influence (DI).

Now TCF is computed as

=(0.65+0.1*DI)

and DI vary from 0-84 and TCF Vary from (0.65-1.35)

so FP=UFP*TCF

Shortcomings of Function Point Metric:

• Subjective Evaluations: It needs subjective evaluation with a lot of judgement involved.

• Conversion need: Many efforts and models are based on LOC, a function point need to be converted.

• Less Researched Data: Less research data is available on function point as compared to LOC.

• Late performance: It is performed after creation of design specification.

• Low Accuracy: It has low accuracy of evaluating as a subjective judgement is involved.

• Long learning curve: As the learning curve is quite long it's not easy to gain proficiency.

• Time consuming: It is a time consuming method as less research data is available which generate low accuracy and less effective results.

4) Feature Point Metric:

A function point extension called feature points, is a superset of the function point measure that can be applied to systems and Engineering software applications. The feature point measure accommodate applications in which Algorithm Complexity is high.

To compute the Feature Point:

Information domain values are again counted and weighted. In addition, another software characteristics- algorithm counted.

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