Se exe 6

Dr. G. Prasuna, Associate Professor, CSE Dept., St. Ann's College of Engineering and Technology, Chirala
SOFTWARE ENGINEERING LAB
JNTUK R20
Exercise 6: FP Oriented Estimation
Ex-6 Consider any application, Calculate effort using
FP oriented estimation model.

Ex-6 Consider any application, Calculate effort using FP oriented estimation model.
Aim: To calculate FP of an application and calculate
effort using FP oriented estimation model.

INTRODUCTION
Function-oriented software metrics use a measure of the functionality delivered
by the application as a normalization value. Since ‘functionality’ cannot be
measured directly, it must be derived indirectly using other direct measures.
Function-oriented metrics were first proposed by Albrecht, who suggested a
measure called the function point. Function points are derived using an empirical
relationship based on countable (direct) measures of software's information
domain and assessments of software complexity.
Function points are computed by completing the table as shown below. Five
information domain characteristics are determined and counts are provided in the
appropriate table location. Information domain values are defined in the
following manner:

Number of user inputs. Each user input that provides distinct application oriented data to thesoftware is
counted. Inputs should be distinguished from inquiries, which are counted separately.
Number of user outputs. Each user output that provides application oriented information tothe user is counted.
In this context output refers to reports, screens, error messages, etc. Individual data items within a report are not
counted separately.
Number of user inquiries. An inquiry is defined as an on-line input that results in the generation of some
immediate software response in the form of an on-line output. Each distinct inquiry is counted.
Number of files. Each logical master file (i.e., a logical grouping of data that may be one partof a large database
or a separate file) is counted.
Number of external interfaces. All machine readable interfaces (e.g., data files on storagemedia) that are used
to transmit information to another system are counted.
Once these data have been collected, a complexity value is associated with each count. Organizations that use
function point methods develop criteria for determining whether a particular entry is simple, average, or
complex. Nonetheless, the determination of complexity is somewhat subjective.

FUNCTION POINTS(FP) COMPUTATION
To compute function points (FP), the following relationship is used:
FP = count total * [0.65 + 0.01 Σ ( Fi)]
where count total is the sum of all FP entries .
The Fi (i = 1 to 14) are "complexity adjustment values" based on responses to the following questions :
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 data entry?
7. Does the on-line data entry require the input transaction to be built over multiple screens or operations?
8. Are the master files updated on-line?
9. Are the inputs, outputs, files, or inquiries complex?
10. Is the internal processing complex?
11. Is the code designed to be reusable?
12. Are conversion and installation 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?

Each of these questions is answered using a scale that ranges from 0 (not important or applicable) to 5 (absolutely
essential). The constant values in Equation and the weighting factors that are applied to information domain counts
are determined empirically.
Once function points have been calculated, they are used in a manner analogous to LOC as a way to normalize
measures for software productivity, quality, and other attributes:
Errors per FP.
Productivity = FP/ Person-Month
Quality = No of faults / FP
Cost= $/FP
 Documentation = Pages count / FP.
ADVANTAGES:
 This method is independent of programming languages.
 It is based on the data which can be obtained in early stage of project
DISADVANTAGES:
This method is more suitable for Business systems and can be developed for that domain
Many aspects of this method are not validated
The functional point has no significant ant meaning, it’s just a numerical value.

Problem:
Given the following values, compute function point when all
complexity adjustment factor (CAF) and weighting factors are average.
User Input = 45
User Output = 50
User Inquiries = 35
User Files = 8
External Interface = 4

Solution:
As complexity adjustment factor is average (given in question), hence, scale = 3.
∑Fi = 14 * 3 = 42 where i=1 to 14
CAF = 0.65 + ( 0.01 * 42 ) = 1.07
As weighting factors are also average (given in question) hence we will multiply
each individual function point to corresponding values in TABLE.
Count_total = (45*4) + (50*5) + (35*4) + (8*10) + (4*7)
= 180 + 250 + 140 + 80 + 28 = 678
Function Point = Count_total *[0.65+(0.01 *∑Fi )] where i=1 to 14
= 678 *[ 0.65 +(0.01 * 42)] = 725.46 FP

To calculate effort using FP oriented estimation model you can use the FP Calculator which is available in
the following URL:
http://groups.umd.umich.edu/cis/course.des/cis525/js/f00/harvey/FP_Calc.html

OUTPUT:

After calculating Function points of an application, we can estimate the effort easily.
For example, if application with 100 Function Points requires 25 PM Effort, we can
estimate the effort for our application having 725.46 Function Points.
For 100 Function Points application Effort is 25 PM
For 725.46 Function Points application Effort = (725.46/100)*25 =181.36 PM

THANK YOU

Se exe 6

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