FP Estimation & Estimated number of FP:

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)]

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, files, or inquires complex

9. Is the internal processing complex?
10. Is the code designed to be reusable?

11. Are masters files updated online?

12. Are conversation 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 functional point has been calculated, productivity, quality, cost, and documentation can be evaluated.

Productivity = FP / person-month

Quality = defects / FP

Cost = $ / FP

Documentation = pages of documentation / FP

Example:

Considering the following information for a software development project to answer the question given below-

Same as LOC

Information Domain Values	Simple	Average	Complex	Count	FP Count
Number of inputs	4	10	16	10	40
Number of outputs	4	5	16	8	40
Number of inquiries	4	12	19	12	48
Number of files	3	6	10	6	60
Number of external interfaces	2	7	3	2	14
Count- Total=					202

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

Factors (Fi)	Value
Backup and recovery	4
Data Communication	1
Distributed processing	0
Performance 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 designed for reuse	0
Conversion / installation in design	1
Multiple installation	3
Application designed for change	5
Sum(Fi)	37

Estimated number of FP:

FP_estimated= count-total * [0.65 + .01 * SUM (Fi)]

                           = 202 * [0.65 + (.01 * 37)]

= 202 * [0.65 + 0.37]

= 202 * 1.02

                = 206.04

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

Productivity = FP / person-month

                FP     = 206.04 / 55.5

                                     = 4 person-month

Total cost = Development cost * person-month

                = $8000 * 4

                 = $32000

Cost per FP = Cost / FP

= $32000/206

                               = $155.34 per FP