Extended Function Point (EFP) Metrics
Function Point (FP) measure was inadequate for many engineering and embedded systems. To overcome this, A number of extensions to the basic function point measure have been proposed. These are as follows:
Feature Points:
- Feature Points are computed by counting the information domain values.
- It can be used in those areas where there is a level of complexity, is comparatively very high.
- Function point (FP) measure is the subset for the Feature point.
- But both the Function point and feature point represents the functionality of the systems
Table for feature point calculation:
Sr. No. |
Measurement Parameter |
Count |
** |
Weighting 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 |
3D function points:
- Data, Functional, and control are three dimensions represented by 3D function points.
- Data: User interfaces and data as in the original method.
- Control: Real-time behavior(s)
- Function: Internal processing
- Data dimension calculation is the same as the FPs. Feature-Transformation is done in the functional dimension. While in the control dimension, feature-Transition is added.
- The 3D Function Point method was proposed by Boeing.
- It is designed to solve two problems with the Albrecht approach.
Example:
Compute the FP, feature point and 3D-function point value for an embedded system with the following characteristics:
1. Internal data structures = 8
2. No. of user inputs = 32
3. No. of user outputs = 60
4. No. of user inquiries = 24
5. No. of external interfaces = 2
6. No. of transformation = 23
7. No. of transition = 32
Assume complexity of the above counts is average case = 3.
Explanation:
Step-1: We draw the Table first for computing FPs.
Sr. No. |
Measurement Parameter |
Count |
** |
Simple Weighting factor |
Average Weighting factor |
Complex Weighting factor |
Calculated Value |
1 |
Number of external inputs(EI) |
32 |
* |
3 |
4 |
6 |
128 |
2 |
Number of external outputs(EO) |
60 |
* |
4 |
5 |
7 |
300 |
3 |
Number of external Inquiries(EQ) |
24 |
* |
3 |
4 |
6 |
96 |
4 |
Number of internal files (ILF) |
8 |
* |
7 |
10 |
15 |
80 |
5 |
Number of external interfaces(EIF) |
2 |
* |
5 |
7 |
10 |
14 |
6 |
Number of Transformation |
23 |
* |
|
|
|
23 |
7 |
Number of Transition |
32 |
* |
|
|
|
32 |
|
Count – Total |
|
|
—–> |
|
|
673 |
Step-2: Find the sum of all fi (1 to 14)
Σ(&fi) = 14 * 3 = 42
Step-3: Calculate the functional point:
FP = Count-total * [0.65 + 0.01 *Σ(&fi) ]
= 618 * [0.65 + 0.01 * 42]
= 618 * [0.65 + 0.42]
= 618 * 1.07
= 661.26
Step-4: Calculate the Feature point:
= (32 *4 + 60 * 5 + 24 * 4 + 80 +14) * 1.07 + {12 * 15 *1.07}
= 853.86
Step-5: Calculate the 3D function point, it is calculated by counting the total calculated values. So, for 3D function points, the required index is 673.
Last Updated :
20 Sep, 2019
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