1. Software Project Estimation IFPUG VS COSMIC
Qasim Babatunde
Md Forhad Rabbi
Shailendra Natraj
Faculty of Informatics

2. Topic Introduction Project estimation IFPUG Methodology COSMIC
Analysis
Convertibility issues
Mapping of concepts between IFPUG and COSMIC
conversion equations
Conclusion
References.

3. Introduction
He who every morning plans the transaction of the day and follows out that plan, carries a thread that will guide him through the maze of the most busy life. But where no plan is laid, where the disposal of time is surrendered merely to the chance of incidence, chaos will soon reign.
Victor Hugo ( )

4. Project Estimation What is Project estimation?
Project estimation can be defined as a project management task which involves three main factors (Time, Cost and effort) and dividing the project life cycle and giving a estimate of time, effort and cost involved in this complete process.
Project Estimation

5. FSM Evolutionary Timeline
ISO FSM standard
COSMIC 2.0
3D FPs
Mk II FP
Feature points
IFPUG 4.0
Mk II
1.3
IFPUG 4.1
Full FPs v.1
COSMIC 3.0
Allan Albrecht
1980
1985
1990
1995
2000
2005
2007
IFPUG 4.2

6. Project Estimation :Methodology
The various Methodology
Estimation Techniques
Methodology
Parametric approach
Heuristic approach
IFPUG’s FPA.
Feature Points.
Cosmic Full Function Points.
Expert Judgment Method.
Thumbs Rule.
Delphi Technique.
Parkinson’s Law.

7. Approach Path IFPUG’s FPA Cosmic Full Function Points IFPUG’s FPA
PC GEEK
Simple Locator
Application
RESULT
(IFPUG’s FPA)
RESULT
Cosmic FFP
RESULT
(IFPUG’s FPA
RESULT
Cosmic FFP
Analysis
Equation

8. advancing software functional size measurement
COSMIC
The Common Software Measurement International Consortium
advancing software functional size measurement

9. IFPUG Lets See IFPUG First… Transaction Function Data Function
External Input
External Output
External Query
Internal Logic File
External Interface File

10. IFPUG      5 Base Functional Component Type Input Output Inquiry
Stored Data
Process
Internal Logical File 
Shared Data
5 Base Functional Component Type
External Logical File

11.   COSMIC Entry  Exit    4 Base Functional Component Type Write
USER
Entry 
Functional Process
Exit   
Write
Read 
Persistence
Data Storage 
4 Base Functional Component Type

12. Lets try with a requirement…
Create new order
File Report System Admin Help Utility

13. Identify Logical File ILF:
Sales
ILF:
Sales= {customer_id, customer_name, order_id, order_date, configuration, quantity}
EIF:
Configuration = {ID, RAM size, board type, CPU speed,….}
Configuration

14. IFPUG: Create new order…
7 DETs
Order Id
Customer id
Customer name
Configuration
Quantity
Price
Action (Submit)
FTR
Sales
Number of DET : 7
Number of FTR : 2
Complexity : Average
Functional Point : 04
FTR
Configurations

15. Complexity of ILF (Sales) Complexity of EIF (Configuration)
DET
RET
COMPLEXITY FP 7 1
Low
Complexity of EIF (Configuration)
DET
RET
COMPLEXITY FP 7 1
Low 5

16. FP Calculation(Using IFPUG): PCGEEK
Function Type
Complexity
Total Complexity
Total EI
7 Low X 3
8 AVG X High X 6 56
157 EQ
9 Low X 3
2 Avg X 4
0 High X 6 35 EO
4 Low X 4
0 Avg X 5
0 High X 7 16
ILF
5 Low X 7
0 Avg X 10
0 High X 15
EIF
3 Low X 5
0 Avg X 7
0 High X 10 15

17. STEPS OF COSMIC… Identify the purpose of measurement
Identify the scope
Identify the functional user
Identify the functional process
Identify data group.
Identify data movement
A (type of) user that is a sender and/or an intended recipient of data in the Functional User Requirements of a piece of software.
A functional process is an elementary component of a set of Functional User Requirements comprising a unique, cohesive and independently executable set of data movements
A data group is a distinct, non empty, non ordered and non redundant set of data attributes where each included data attribute describes a complementary aspect of the same object of interest.
A base functional component which moves
a single data group type

18. COSMIC: Create Customer Order..
2 Data Groups
Order Id
Customer id
Customer name
Quantity
Price
Action (Submit)
Entry
Functional Process
Write
Entry 
Configuration
Persistence Data Storage
Exit 

19. Order data group to process
Entry
Order data group to process
Order Id, Customer Name, Customer id, Quantity, Price
Configuration data group to process
Configuration; e.g. HDD, RAM, Monitor, etc.

20. No READ operation has been performed

21. WRITE..
Order detail to stored data
Configuration detail to stored data

22. EXIT
Return a conformation message to user

23. Functional Point : Create Customer Order
Size = 2 Entry+2 Write+1 Exit = 5 FP it

24. FP Calculation (Using COSMIC): PCGEEK
Size (functional process)=Σ size (Entries) + Σ size (Exits) + Σ size (Reads) + Σ size (Writes)
Size (functional process) =
Σ size ( ) +
Σ size ( ) +
Σ size ( ) +
Σ size ( )
= 97

25. IFPUG: SIMPLE LOCATOR APPLICATION

26. COSMIC: SIMPLE LOCATOR APPLICATION
Size (functional process) = Σ size (Entries) + Σ size
(Exits) + Σ size (Reads) + Σ size (Writes)
Size (functional process) = ( ) + ( ) + ( ) + ( ) =
=19 cfp

27. Process… IFPUG: Files accessed (FTR) COSMIC: Sub Process (Read, Write)
IFPUG: Logical Files
Stored data
Cosmic: Persistent Data
Stored data
IFPUG: DETs crossing boundary
COSMIC: Sub process (Entry, Exit)

28. Why COSMIC?
Firstly, it should be noted that most, if not all FPA methods are based on heuristics rather than engineering principles!! except COSMIC
This section will try to compare the IFPUG and the COSMIC methods, in terms of applicability.
Cosmic 28

29. Business applications Infrastructure systems
Why COSMIC contd….
Application types
Business applications
Embedded systems
Infrastructure systems
Real time systems
Hybrid
Cosmic

30. Measurement viewpoint.
Why COSMIC contd….
Measurement viewpoint.
User view (does this describe all the functionality of the system?) e.g. multiprocessor system, functionality of software beneath the application layer, batch processing stream.
Measurement and scale validity
Weighted complexity with limits
No limits and all EP equal
Ordinal VS ratio
COSMIC provides very good estimates close to the actual effort spent in each project.
Cosmic 30

31. Convertibility issues
A lot of organizations have invested a lot into IFPUG and it is hard and unwise to scrap such investments over-night
But note: the COSMIC [2003]says:
“…that an average conversion formula to convert COSMIC FFP size to an IFPUG FPA size would be grossly misleading.”
Cosmic 31

32. Convertibility issues Contd…
However, the same also says:
“….therefore the only means of conversion will be to measure some sizes of software developed or enhanced in similar circumstances on both the IFPUG and COSMIC methods and to establish an empirical conversion formula.”
Cosmic 32

33. Mapping of concepts between IFPUG and COSMIC
Purpose of measurement
Purpose of the count
Scope of a measurement 
Scope of the count 
Boundary 
Application boundary 
User 
Object of interest 
Entity 
Data group 
File 
Data attribute 
Data elements 
Functional process 
Transactional function 
Comparision 33

34. Suggested conversion equations
Authors
Convertibility Equation
Fetcke
Y(Cfsu) = 1.1 * (UFP) - 7.6/6.2
Letherthuis ≤200 FP
Y(Cfsu) = 1.2 * (UFP) -87
Letehrthuis ≥200 FP
Y(Cfsu) = 0.75 * (UFP) -2.6
Desharnais 2005
Y(Cfsu) = 0.84 * (UFP) + 18
Abran [1999]
Y(Cfsu) = 1.0* (UFP) - 3
Cuadrado et al. [2007]
see next slide
Convertibility equation 34

35. 35

36. Empirical illustration36

37. Further analysis with own data
Equation
PCGEEK (157, 97)
Locator (46,19)
Fetcke
166 43
Letherthuis ≤ 200 FP
102
-38
Letehrthuis ≥ 200 FP
116 32
Desharnais [2005]
150 57
Abran [1999]
155
Cuadrado et al. [2007]
77 ≤ 97 ≤ 99
19 ≤ 19 ≤ 25
Data analysis 37

38. It can not measure mathematical algorithms.
Current travails
Benchmark data are not as widely available as is the case for FPA. But this is a matter of time!!
It can not measure mathematical algorithms. 38

39. Conclusion
More investigation and research is necessary to deal with the convertibility among different FSM methods, mapping between Unified Modeling Language and FP, measuring the domain of algorithmic/ scientific software, measurement of functional reuse and automating FSM process.
Conclusion 39

40. Refences [1] Vogelezang, F., One year experience with COSMIC-FFP,
Software Measurement European Forum – SMEF 2005, Rome
(Italy), 2005.
[2] The COSMIC Functional Size Measurement Method Version
3.0, Measurement Manual, Common Software Measurement
International Consortium, September 2007
[3] IFPUG, Function point counting practices manual Tech.
rep., The International Function Point Users Group, 2000. 40