1

Cost Estimation

CIS 375

Bruce R. Maxim

UM-Dearborn

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

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

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

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

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

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

Function PointsParameter 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

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

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

Static Linear Models

Often built using regression analysis

Effort = c0 + ci * xi

C = regression coefficient

X = product or process attribute

12

Static Non-Linear Models

ExamplesEffort = c0 + ci * xi

di

Ci and di are non-linear regression constants

orEffort = (a + b S C) * m(X)

where S is size in KLOC

a, b, and c are regression constants

13

Halstead’s Software Science

Assumptions

• complete algorithm specification exists

• programmer works alone

• programmer knows what to do

• Based onN = # of unique operators

n = # of unique operands

14

Halstead Equations

Effort E = N2 * log2 (n) / 4

To compute NN = k * Ss

k = average # operators per LOC

k is language specific

To compute nN = n * log2 (n / 2)

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

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

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

Basic COCOMO

• Computes software development effort (and cost) as a function of program size, expressed in estimated lines of code.

• m(x) = 1

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

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

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

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

Putnam Model

Based on Rayliegh curve - > skewed, median & mean offset from one another

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

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

Parr Model

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

Jensen Model

• Putnam variation

• Less sensitive to schedule compression than Putnam

S = Cte * T * K1/2

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

Dynamic Model Problems

• Still rely on expert judgment

• Not clear that all project costs have been accommodated here either