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FP Estimation

Function oriented metrics focus on program "functionality" or "utility". Albrecht first proposed function point method, which is a function oriented productivity measurement approach.

Five information domain characteristics are determined and counts for each are provided and recorded in a table.

  • Number of user inputs
  • Number of user outputs
  • Number of user inquires
  • Number of files
  • Number of external interfaces

Once the data have been collected, a complexity value is associated with each count. Each entry can be simple, average or complex. Depending upon these complexity values is calculated.

To compute function points, we use

FP = count-total X [ 0.65 + 0.01 * SUM(Fi) ]

Where, count-total is the sum of all entries obtained from fig. 3.2

Fi(I= 1 to 14) are complexity adjustment values based on response to questions(1-14) given below. The constant values in the equation and the weighing factors that are applied to information domain are determined empirically.

Fi
1. Does the system require reliable backup and recovery?
2. Are data communications required?
3. Are there distributed processing functions?
4. Is performance critical?
5. Will the system run in an existing, heavily utilized operational environment?
6. Does the system require on-line entry?
7. Does the on-line data entry require the input transaction to be built over multiple screens or operations?
8. Are the inputs, outputs, files, or inquiries complex
9. Is the internal processing complex?
10. Is the code designed to be reusable?
11. Are master files updated on-line?
12. Are conversion and installations included in the design?
13. Is the system designed for multiple installations in different organizations?
14. Is the application designed to facilitate change and ease of use by the user?

Rate each factor on a scale of 0 to 5

0 - No influence
1 - Incidental
2 - Moderate
3 - Average
4 - Significant
5 - Essential

Count-total is sum of all FP entries.

Once function points have been calculated, productivity, quality, cost and documentation can be evaluated.

Productivity = FP / person-month
Quality = defects / FP
Cost = $ / FP
Documentation = pages of documentation / FP

Computing function-point metrics
Fig 3.2 - Computing function-point metrics

Example: Same as LOC problem

Information Domain Values
Opt likely Pess est Account wt FP Account
Number of Inputs
4
10
16
10
4
40
Number of Outputs
4
7
16
8
5
40
Number of Inquiries
5
12
19
12
4
48
Number of Files
3
6
9
6
10
60
Number of external interfaces
2
2
3
2
7
14
Count Total
202

Complexity weighing factors are determined and the following results are obtained.

Factor
Value
Backup Recovery
4
Data Communication
1
Distributed processing
0
Perfomance Critical
3
Existing Operating Environment
2
On-line data entry
5
Input transaction over multiple screens
5
Master file updated online
3
Information domain values complex
3
Internal processing complex
2
Code design for reuse
0
Conversion/Installation in design
1
Multiple installations
3
Application designed for change
5
Sum ( Fi )
37

Estimated number of FP :

FPestimated = count-total * [0.65 + .01 * sum(Fi) ]
FPestimated = 206

From historical data, productivity is 55.5 FP/person-month and development cost is $8000 per month.

Productivity = FP/ person-month
person-month = FP/Productivity
= 202/55.5

= 3.64 person-month

Total Cost = Development cost * person-month
= 8000 * 3.64
=$29100

Cost per FP = Cost/FP
= 29100/202
= $144.2per FP


  

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