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.