Project analysis

1. 1
Cost Estimation
CIS 375
Bruce R. Maxim
UM-Dearborn

2. 2
Types of Cost Models
• Experiential
– derived from past experience
• Static
– derived using “regression” techniques
– doesn’t change with time
• Dynamic
– derived using “regression” techniques
– often includes the effects of time

3. 3
Expert Guessing
A = The most pessimistic estimate.
B = The most likely estimate.
C = The most optimistic estimate.
Ê = (A + 4B + C)
6
(Weighted average; where Ê = estimate).

4. 4
Delphi Technique
1. Group of experts, make "secret" guesses.
2. "secret" guesses are used to compute
group average.
3. Group average is presented to the group.
4. Group, once again makes "secret" guesses.
5. Individual guesses are again averaged.
6. If new average is different from previous,
then goto (4).
7. Otherwise Ê = new average.

5. 5
Wolverton Model - 1
Uses a software type matrix where the
column headings come from the cross
product
{old, new} X {easy, moderate, hard}
For example:
Type OE OM OH NE NM NH
Control 21 27 30 33 40 49
I/O

6. 6
Wolverton Model -2
• Estimate models in terms of LOC:
C(k) = Ss(k) * Ci,j(k)
Cost of = Size matrix cost entry
module k = of module k for modules like K
System Cost =  C(k) where k = 1 to n

7. 7
Problems with Expert Judgement
• It is subjective. (consensus is difficult to
achieve)
• Extrapolating from one project to
another may be difficult.
• Users and project managers tend not to
estimate costs very well.
• Cost matrices require periodic updates.

8. 8
Function Points
Parameter Simple + Average + Complex = Fi
Distinct input items 3( ) + 4( ) + 6( ) = ?
Output screens/reports 4( ) + 5( ) + 7( ) = ?
Types of user queries 3( ) + 4( ) + 6( ) = ?
Number of files 7( ) + 10( ) + 15( ) = ?
External interface 5( ) + 7( ) + 10( ) = ?
Total = ?

9. 9
Function Point Equation
F.P.’s = T * (0.65 + 0.01 * Q)
T = unadjusted table total
Q = score from questionnaire
(14 items with values 0 to 5)
• Cost of producing one function point? May be
organization specific.

10. 10
Function Point Questionnaire
1. Backup.
2. Data communication.
3. Distributed processes.
4. Optimal performance.
5. Heavily used
operating system.
6. On-line data security.
7. Multiple screens.
8. On-line master file
update.
9. Complex inputs,
queries, outputs.
10. Complex internal
processing.
11. Reusable code.
12. Conversion or
installation.
13. Multiple user sites.
14. Ease of use.

11. 11
Static Linear Models
Often built using regression analysis
Effort = c0 +  ci * xi
C = regression coefficient
X = product or process attribute

12. 12
Static Non-Linear Models
Examples
Effort = c0 +  ci * xi
di
Ci and di are non-linear regression constants
or
Effort = (a + b S C) * m(X)
where S is size in KLOC
a, b, and c are regression constants

13. 13
Halstead’s Software Science
Assumptions
• complete algorithm specification exists
• programmer works alone
• programmer knows what to do
• Based on
N = # of unique operators
n = # of unique operands

14. 14
Halstead Equations
Effort
E = N2 * log2 (n) / 4
To compute N
N = k * Ss
k = average # operators per LOC
k is language specific
To compute n
N = n * log2 (n / 2)

15. 15
Watson and Felix Model
E = 5.25 * S 0.91
composite productivity factor
p =  wi * xi
L = LOC per person-month = f(p)
E = S / L

16. 16
Bailey and Basili Model
E` = 5.5 + 0.73 * S 1.6
R = E / E’ = actual effort/predicted effort
Adjusted effort is
ERadj = R – 1 if R >= 1
= 1 – 1/R if R < 1
Eadj = (1 + ERadj) * E if R >= 1
= E / (1 + ERadj) if R < 1

17. 17
COCOMO - I
• Model E = a Sb * m(x)
BASIC INTERMEDIATE
MODE a b a b
Organic 2.4 1.05 3.2 1.05
Semidetached 3.0 1.12 3.0 1.12
Embedded 3.6 1.20 2.8 1.20

18. 18
Basic COCOMO
• Computes software development effort
(and cost) as a function of program size,
expressed in estimated lines of code.
• m(x) = 1

19. 19
Intermediate COCOMO
• Computes software development effort
as a function of program size and a set
of "cost drivers" that include subjective
assessments of product, hardware,
personnel, and project attributes.
• m(x) =  m(xi)

20. 20

21. 21
Detailed COCOMO
• Includes all characteristics of the
intermediate version with an
assessment of the cost driver’s impact
on each step (analysis, design, ect.) of
the software engineering process
• m(x) based on similar questionnaire

22. 22
Static Model Problems
• Existing models rely at least in part on
expert judgment
• Most static estimates require estimation
of the product in lines of code (LOC)
• Not clear which cost factors are
significant in all development
environments

23. 23
Dynamic Models
• It is helpful to know when effort will be
required on a project as well as how
much total effort is required
• Most models are time or phase
sensitive in their effort computations

24. 24
Putnam Model
Based on Rayliegh curve - > skewed, median & mean
offset from one another

25. 25
Putnam Model Details
• Volume of work
• Difficulty gradient for measuring complexity
• Project technology factor measuring staff
experience
• Delivery time constraints
• Staffing model based on
– Total cumulative staff
– How quickly new staff can be absorbed
– # days in project month

26. 26
Putnam Equations
E = y(T) = 0.3945 * K
K = area under curve [0 , 1)
measured in programmer year
T = optimal development time in years
D = K / T2 difficulty
P = ci * D –2/3 productivity
S = c * K –1/3 * T 4/3 lines of code

27. 27
Parr Model

28. 28
Parr Equation
• Putnam variation
• Staff may already be familiar with
project tools, methods, and
requirements
Staff(t) = (sech2 (a*t + c) / 2) / 4

29. 29
Jensen Model
• Putnam variation
• Less sensitive to schedule compression
than Putnam
S = Cte * T * K1/2

30. 30
Cooperative Programming Model
• Includes size of project team in estimate as well as code
size
E = E1(S) + E2(M)
S = code size
M = average # team members
E1(S) = a + b * S
effort of single team member
E2(M) = c * Md
effort required for coordination with other members

31. 31
Dynamic Model Problems
• Still rely on expert judgment
• Not clear that all project costs have
been accommodated here either