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Extended Function Point (EFP) Metrics

FP metric has been further extended to compute:

1. Feature points.
2. 3D function points.

Feature Points

1. Feature point is the superset of function point measure that can be applied to systems and engineering software applications.
2. The feature points are used in those applications in which the algorithmic complexity is high like real-time systems where time constraints are there, embedded systems, etc.
3. Feature points are computed by counting the information domain values and are weighed by only single weight.
4. Feature point includes another measurement parameter-ALGORITHM.
5. The table for the computation of feature point is as follows:

Feature Point Calculations

Measurement Parameter	Count		Weighing factor
1. Number of external inputs (EI)	–	*	4	–
2. Number of external outputs (EO)	–	*	5	–
3. Number of external inquiries (EQ)	–	*	4	–
4. Number of internal files (ILF)	–	*	7	–
5. Number of external interfaces (EIF)	–	*	7	–
6.Algorithms used Count total →	–	*	3	–

The feature point is thus calculated with the following formula:

                FP = Count-total * [0.65 + 0.01 *∑(f_i)]
                    = Count-total * CAF

where count-total is obtained from the above table.

                CAF = [0.65 + 0.01 * ∑(f_i)]

and ∑(f_i) is the sum of all 14 questionnaires and show the complexity adjustment value/factor-CAF (where i ranges from 1 to 14). Usually, a student is provided with the value of ∑(f_i) .

6. Function point and feature point both represent systems functionality only.

7. For real-time applications that are very complex, the feature point is between 20 and 35% higher than the count determined using function point above.

3D function points

Three dimensions may be used to represent 3D function points?data dimension, functional dimension, and control dimension.

2. The data dimension is evaluated as FPs are calculated. Herein, counts are made for inputs, outputs, inquiries, external interfaces, and files.

3. The functional dimension adds another feature-Transformation, that is, the sequence of steps which transforms input to output.

4. The control dimension that adds another feature-Transition that is defined as the total number of transitions between states. A state represents some externally observable mode

Now f_i for average case = 3. So sum of all f_i (i ←1 to 14) = 14 * 3 = 42
                                FP = Count-total * [0.65 + 0.01 *∑(f_i ) ]
                                = 618 * [0.65 + 0.01 * 42]
                                = 618 * [0.65 + 0.42]
                                = 618 * 1.07 = 661.26

and feature point = (32 *4 + 60 * 5 + 24 * 4 + 80 +14) * 1.07 + {12 * 15 *1.07}
                            = 853.86

Example: Compute the 3D-function point value for an embedded system with the following characteristics:

1. Internal data structures = 6
2. External data structures = 3
3. No. of user inputs = 12
4. No. of user outputs = 60
5. No. of user inquiries = 9
6. No. of external interfaces = 3
7. Transformations = 36
8. Transitions = 24

Assume complexity of the above counts is high.

Solution: We draw the Table first. For embedded systems, the weighting factor is complex and complexity is high. So,

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