Software cost estimation

Predicting the resources
required for a software
development process
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 1
Objectives




To introduce the fundamentals of software costing
and pricing
To describe three metrics for software
productivity assessment
To explain why different techniques should be
used for software estimation
To describe the COCOMO 2 algorithmic cost
estimation model
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 2
Topics covered




Productivity
Estimation techniques
Algorithmic cost modelling
Project duration and staffing
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 3
Fundamental estimation questions




How much effort is required to complete an
activity?
How much calendar time is needed to complete
an activity?
What is the total cost of an activity?
Project estimation and scheduling and interleaved
management activities
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 4
Software cost components



Hardware and software costs
Travel and training costs
Effort costs (the dominant factor in most
projects)
•
•

salaries of engineers involved in the project
Social and insurance costs
Effort costs must take overheads into account
•
•
•
costs of building, heating, lighting
costs of networking and communications
costs of shared facilities (e.g library, staff restaurant, etc.)
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 5
Costing and pricing



Estimates are made to discover the cost, to the
developer, of producing a software system
There is not a simple relationship between the
development cost and the price charged to the
customer
Broader organisational, economic, political and
business considerations influence the price
charged
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 6
Software pricing factors
Factor
Market op portu nity
Cost estimate u ncertain ty
Con tractual terms
Requ iremen ts vo latility
Fin an cial h ealth
©Ian Sommerville 2000
Description
A d ev elo pment o rg anisation may q uo te a lo w
price
because it wish es to move
into a new segment of the
software market. Accep ting a lo w p ro fit o n
on e
project may giv e the op portunity
of mo re profit later.
The experien ce g ain ed may allow new produ cts to be
dev elo ped.
If an organ isatio n is unsu re of its cost estimate, it
may increase its price by so me con tin gency o ver and
abo ve its no rmal p ro fit.
A customer may be
willin g to allo w the develop er to
retain ownersh ip of th e sou rce code and reuse it
in
other pro jects. Th e p rice ch arg ed may
then be less
than if the software source co de is h an ded o ver to the
custo mer.
If th e
req uirements are likely to ch ange, an
organ isatio n may lower its price to win a co ntract.
After the co ntract is award ed, hig h prices may be
charg ed fo r chan ges to the req uirements.
Dev elopers in financial difficulty
may lower th eir
price to gain a con tract. It is better to make
a small
profit o r break even than to g o ou t of bu sin ess.
Software Engineering, 6th edition. Chapter 23
Slide 7
Programmer productivity



A measure of the rate at which individual
engineers involved in software development
produce software and associated
documentation
Not quality-oriented although quality assurance
is a factor in productivity assessment
Essentially, we want to measure useful
functionality produced per time unit
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 8
Productivity measures


Size related measures based on some output from
the software process. This may be lines of
delivered source code, object code instructions,
etc.
Function-related measures based on an estimate
of the functionality of the delivered software.
Function-points are the best known of this type of
measure
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 9
Measurement problems



Estimating the size of the measure
Estimating the total number of programmer
months which have elapsed
Estimating contractor productivity (e.g.
documentation team) and incorporating this
estimate in overall estimate
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 10
Lines of code

What's a line of code?
•
•


The measure was first proposed when programs were typed on
cards with one line per card
How does this correspond to statements as in Java which can
span several lines or where there can be several statements on
one line
What programs should be counted as part of the
system?
Assumes linear relationship between system
size and volume of documentation
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 11
Productivity comparisons

The lower level the language, the more
productive the programmer
•

The same functionality takes more code to implement in a
lower-level language than in a high-level language
The more verbose the programmer, the higher
the productivity
•
Measures of productivity based on lines of code suggest that
programmers who write verbose code are more productive than
programmers who write compact code
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 12
System development times
Assembly code
High-level language
Assembly code
High-level language
©Ian Sommerville 2000
Analysis
3 weeks
3 weeks
Size
5000 lines
1500 lines
Design
Coding
Testing
Documentation
5 weeks
8 weeks
10 weeks
2 weeks
5 weeks
8 weeks
6 weeks
2 weeks
Effort
Productivity
28 weeks
714 lines/month
20 weeks
300 lines/month
Software Engineering, 6th edition. Chapter 23
Slide 13
Function points

Based on a combination of program
characteristics
•
•
•
•


external inputs and outputs
user interactions
external interfaces
files used by the system
A weight is associated with each of these
The function point count is computed by
multiplying each raw count by the weight and
summing all values
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 14
Function points


Function point count modified by complexity of
the project
FPs can be used to estimate LOC depending on
the average number of LOC per FP for a given
language
•
•

LOC = AVC * number of function points
AVC is a language-dependent factor varying from 200-300 for
assemble language to 2-40 for a 4GL
FPs are very subjective. They depend on the
estimator.
•
Automatic function-point counting is impossible
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 15
Object points



Object points are an alternative function-related
measure to function points when 4Gls or similar
languages are used for development
Object points are NOT the same as object classes
The number of object points in a program is a
weighted estimate of
•
•
•
The number of separate screens that are displayed
The number of reports that are produced by the system
The number of 3GL modules that must be developed to
supplement the 4GL code
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 16
Object point estimation


Object points are easier to estimate from a
specification than function points as they are
simply concerned with screens, reports and 3GL
modules
They can therefore be estimated at an early point
in the development process. At this stage, it is
very difficult to estimate the number of lines of
code in a system
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 17
Productivity estimates




Real-time embedded systems, 40-160
LOC/P-month
Systems programs , 150-400 LOC/P-month
Commercial applications, 200-800
LOC/P-month
In object points, productivity has been measured
between 4 and 50 object points/month depending
on tool support and developer capability
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 18
Factors affecting productivity
Factor
Application domain
experience
Process quality
Project size
Technology support
Working environment
©Ian Sommerville 2000
Description
Knowledge of the application domain is essential
for
effective software development. Engineers who
already
understand a domain are
likely to be the most
productive.
The development process used can have
a significant
effect on productivity. This is covered in Chapter 31.
The larger a project, the more time required for
team
communications.
Less time is available for
development so individual productivity is reduced.
Good support technology such as CA SE tools,
supportive configuration management systems, etc.
can improve productivity.
As discussed in Chapter 28, a quiet working
environment with private work areas
contributes to
improved productivity.
Software Engineering, 6th edition. Chapter 23
Slide 19
Quality and productivity




All metrics based on volume/unit time are
flawed because they do not take quality into
account
Productivity may generally be increased at the
cost of quality
It is not clear how productivity/quality metrics
are related
If change is constant then an approach based on
counting lines of code is not meaningful
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 20
Estimation techniques

There is no simple way to make an accurate
estimate of the effort required to develop a
software system
•
•
•

Initial estimates are based on inadequate information in a user
requirements definition
The software may run on unfamiliar computers or use new
technology
The people in the project may be unknown
Project cost estimates may be self-fulfilling
•
The estimate defines the budget and the product is adjusted to
meet the budget
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 21
Estimation techniques





Algorithmic cost modelling
Expert judgement
Estimation by analogy
Parkinson's Law
Pricing to win
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 22
Algorithmic code modelling


A formulaic approach based on historical cost
information and which is generally based on the
size of the software
Discussed later in this chapter
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 23
Expert judgement



One or more experts in both software
development and the application domain use
their experience to predict software costs.
Process iterates until some consensus is
reached.
Advantages: Relatively cheap estimation
method. Can be accurate if experts have direct
experience of similar systems
Disadvantages: Very inaccurate if there are no
experts!
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 24
Estimation by analogy



The cost of a project is computed by comparing
the project to a similar project in the same
application domain
Advantages: Accurate if project data available
Disadvantages: Impossible if no comparable
project has been tackled. Needs systematically
maintained cost database
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 25
Parkinson's Law
“work expands to fill the time available”

The project costs whatever resources are
available
•


Software due in 12 months, 5 people available
=> 60-person-months of effort
Advantages: No overspend
Disadvantages: System is usually unfinished
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 26
Pricing to win



The project costs whatever the customer has to
spend on it
Advantages: You get the contract
Disadvantages: The probability that the
customer gets the system he or she wants is
small. Costs do not accurately reflect the work
required
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 27
Top-down and bottom-up estimation


Any of these approaches may be used top-down
or bottom-up
Top-down
•

Start at the system level and assess the overall system
functionality and how this is delivered through sub-systems
Bottom-up
•
Start at the component level and estimate the effort required for
each component. Add these efforts to reach a final estimate
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 28
Top-down estimation



Usable without knowledge of the system
architecture and the components that might be
part of the system
Takes into account costs such as integration,
configuration management and documentation
Can underestimate the cost of solving difficult
low-level technical problems
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 29
Bottom-up estimation



Usable when the architecture of the system is
known and components identified
Accurate method if the system has been designed
in detail
May underestimate costs of system level activities
such as integration and documentation
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 30
Estimation methods





Each method has strengths and weaknesses
Estimation should be based on several methods
If these do not return approximately the same
result, there is insufficient information available
Some action should be taken to find out more in
order to make more accurate estimates
Pricing to win is sometimes the only applicable
method
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 31
Experience-based estimates


Estimating is primarily experience-based
However, new methods and technologies may
make estimating based on experience inaccurate
•
•
•
•
•
Object oriented rather than function-oriented development
Client-server systems rather than mainframe systems
Off the shelf components
Component-based software engineering
CASE tools and program generators
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 32
Pricing to win




This approach may seem unethical and unbusinesslike
However, when detailed information is lacking it may be
the only appropriate strategy
The project cost is agreed on the basis of an outline
proposal and the development is constrained by that cost
A detailed specification may be negotiated or an
evolutionary approach used for system development
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 33
Algorithmic cost modelling

Cost is estimated as a mathematical function of
product, project and process attributes whose
values are estimated by project managers
Effort = A  SizeB  M
A is an organisation-dependent constant, B reflects the disproportionate
effort for large projects and M is a multiplier reflecting product,
process and people attributes


Most commonly used product attribute for cost
estimation is code size
Most models are basically similar but with
different values for A, B and M
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 34
Estimation accuracy


The size of a software system can only be known
accurately when it is finished
Several factors influence the final size
•
•
•


Use of COTS and components
Programming language
Distribution of system
As the development process progresses then the
size estimate becomes more accurate
Size may be estimated by analogy with other
projects, by converting FP to size, etc.
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 35
Estimate uncertainty
4x
2x
x
F easi bi li ty R equ irem en ts
Des ig n
C o de
Del iv ery
0 .5x
0 .25 x
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 36
The COCOMO model




An empirical model based on project experience
Well-documented, ‘independent’ model which is
not tied to a specific software vendor
Long history from initial version published in
1981 (COCOMO-81) through various
instantiations to COCOMO 2
COCOMO 2 takes into account different
approaches to software development, reuse, etc.
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 37
COCOMO 81
D escr iption
F o rm u la
Pr o je ct
co m p le x ity
S imp le
P M = 2 .4 (K D SI ) 1 .0 5  M
M o de ra te
P M = 3 .0 (K D SI ) 1 .1 2  M
E m be dd ed
P M = 3 .6 (K D SI ) 1 .2 0  M
W el l-u nd ers to od ap p lica tio ns
d eve lop ed by sm all te ams .
M or e c om plex pr oject s w h er e
te am m em be rs ma y h av e l im it e d
ex pe rie nc e of r ela te d s ystems .
C om plex project s w h ere t h e
soft wa re is p ar t of a s tron gly
co up le d com plex of ha rd w a re ,
soft wa re, re gu lat ions an d
o pe ration al proc ed u res .
KDSI: Thousands of Delivered Source Instructions
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 38
COCOMO 2 levels


COCOMO 2 is a 3 level model that allows
increasingly detailed estimates to be prepared as
development progresses
Early prototyping level
•

Early design level
•

Estimates based on object points and a simple formula is used for
effort estimation
Estimates based on function points that are then translated to LOC
Post-architecture level
•
Estimates based on lines of source code
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 39
Early prototyping level




Supports prototyping projects and projects where
there is extensive reuse
Based on standard estimates of developer
productivity in object points/month
Takes CASE tool use into account
Formula is
•
PM = ( NOP  (1 - %reuse/100 ) ) / PROD
•
PM is the effort in person-months, NOP is the number of object
points and PROD is the productivity
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 40
Object point productivity
Deve lop e r’s
expe ri enc e and
cap a bi lit y
ICA S E m aturity and
cap a bi lit y
P ROD ( NO P /m on th)
©Ian Sommerville 2000
Ve ry low
L ow
No mi na l
H igh
Ve ry h igh
Ve ry low
L ow
No mi na l
H igh
Ve ry h igh
4
7
13
Software Engineering, 6th edition. Chapter 23
25
50
Slide 41
Early design level


Estimates can be made after the requirements have been agreed
Based on standard formula for algorithmic models
•
PM = A  SizeB  M + PMm where
•
M = PERS  RCPX  RUSE  PDIF  PREX  FCIL  SCED
(product and process multipliers)
•
PMm = (ASLOC  (AT/100)) / ATPROD
•
A = 2.5 in initial calibration, Size in KLOC, B varies from 1.1 to 1.24
depending on novelty of the project, development flexibility, risk
management approaches and the process maturity
ASLOC: automatically generated lines of source code
AT: % of total system code that is automatically generated
ATPROD: productivity level for this type of code generation
•
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 42
Multipliers

Multipliers reflect the capability of the developers, the
non-functional requirements, the familiarity with the
development platform, etc.
•
•
•
•
•
•
•

RCPX - product reliability and complexity
RUSE - the reuse required
PDIF - platform difficulty
PREX - personnel experience
PERS - personnel capability
SCED - required schedule
FCIL - the team support facilities
[1(low) to 6(high) scale]
PM reflects the amount of automatically generated code
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 43
Post-architecture level


Uses same formula as early design estimates
Estimate of size is adjusted to take into account
•
•
•
Requirements volatility. Rework required to support change
Extent of possible reuse. Reuse is non-linear and has associated
costs so this is not a simple reduction in LOC
ESLOC = ASLOC  (AA + SU +0.4DM + 0.3CM +0.3IM)/100
» ESLOC is equivalent number of lines of new code. ASLOC is the
number of lines of reusable code which must be modified, DM is the
percentage of design modified, CM is the percentage of the code that is
modified , IM is the percentage of the original integration effort
required for integrating the reused software.
» SU is a factor based on the cost of software understanding, AA is a
factor which reflects the initial assessment costs of deciding if software
may be reused.
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 44
The exponent term


This depends on 5 scale factors (see next slide).
Their sum/100 is added to 1.01
Example
•
•
•
•
•

Precedenteness - new project - 4
Development flexibility - no client involvement - Very high - 1
Architecture/risk resolution - No risk analysis - V. Low - 5
Team cohesion - new team - nominal - 3
Process maturity - some control - nominal - 3
Scale factor is therefore 1.17
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 45
Exponent scale factors
S ca le fac tor
P re ceden tedn e ss
Deve lop m en t
fl ex ibilit y
A rch it ec ture/ris k
reso luti on
T ea m c ohes ion
P ro c es s m aturit y
©Ian Sommerville 2000
Ex pl an atio n
R e flec ts the prev ious expe ri ence of t he o rgan is ati on
w it h this type of proj e ct. Ve ry low m eans no p rev iou s
expe ri enc e , Ex tr a h igh m eans t ha t the organ isa ti on i s
co m pletely fa mili ar w ith t his app li ca ti on do m ain.
R e flec ts the deg ree of fle xib ilit y in t he d e ve lop m en t
proces s. Ve ry low m ean s a pr e sc ri bed proc e ss is us e d;
E xt ra h igh m ean s tha t t he cl ie n t on ly sets gene ral goa ls.
R e flec ts the ex ten t of ris k an a ly si s ca rrie d ou t. Ve ry low
m ean s littl e a na lys is, E xtr a h igh m eans a co m plete a
tho rough ri sk ana lys is .
R e flec ts how we ll th e deve lop m en t t ea m know e a ch
othe r and wo rk toge the r. Ve ry low m ean s ve ry di ffi cu lt
interac ti ons , E xtra h igh me ans an integ rated and
effe cti ve team w it h no co mm un ica ti on p rob lems .
R e flec ts the proces s m atu rit y o f the organ isa ti on . T he
co m pu tati on o f th is v a lu e dep e nds on the CM M
M at ur it y Q ues ti onna ir e bu t an es tim ate can be a c hi e ved
by sub tr ac ti ng the C M M proc e ss m aturity l eve l from 5 .
Software Engineering, 6th edition. Chapter 23
Slide 46
Multipliers

Product attributes
•

Computer attributes
•

constraints imposed on the software by the hardware platform
Personnel attributes
•

concerned with required characteristics of the software product
being developed
multipliers that take the experience and capabilities of the
people working on the project into account.
Project attributes
•
concerned with the particular characteristics of the software
development project
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 47
Project cost drivers
P roduct attributes
R E LY
R equired system reliability
C P LX
C om plexity
of
system
m odules
DOCU
E xtent
of
docum entation
required
C om puter attributes
T IM E
E xecution tim e constraints
PVOL
V olatility of developm ent
platform
P ersonnel attributes
ACAP
C apability of project analysts
PCON
P ersonnel continuity
PEXP
P rogram m er experience
project dom ain
P roject attributes
TOOL
U se of softw are tools
SC E D
©Ian Sommerville 2000
in
DATA
R U SE
Size of database used
R equired percentage of reusable
com ponents
ST O R
M em ory constraints
PCAP
AEXP
P rogram m er capability
A nalyst experience in project
dom ain
Language and tool experience
LT E X
SIT E
E xtent of m ulti-site w orking and
quality of site com m unications
D evelopm ent schedule
com pression
Software Engineering, 6th edition. Chapter 23
Slide 48
Effects of cost drivers
E x po ne n t v al u e
S ystem siz e (in cl u ding fa ct o rs fo r re us e
an d req uire m e nts vo la til ity )
In itia l CO C OMO estim ate w ith out
cos t dr iv e rs
R e li a bi lity
C om plex ity
M em ory c ons trai n t
T o ol use
S ch ed u le
A djus te d C OCO M O estima te
R e li a bi lity
C om plex ity
M em ory c ons trai n t
T o ol use
S ch ed u le
A djus te d C OCO M O estima te
©Ian Sommerville 2000
1 .1 7
1 28 , 0 00 D SI
7 30 p er son- m o nths
V er y h ig h, m u ltip li e r = 1.39
V er y h ig h, m u ltip li e r = 1.3
H ig h, m u ltip li e r = 1.21
L o w, m ultip lier = 1 .12
A cce ler a te d , mu lt iplie r = 1. 2 9
2 30 6 p er son -m on ths
V er y l ow , mu lt iplie r = 0. 7 5
V er y l ow , mu lt iplie r = 0. 7 5
N on e, mu lt iplie r = 1
V er y h ig h, m u ltip li e r = 0.72
N orm al, m ultip lier = 1
2 95 p er son- m o nths
Software Engineering, 6th edition. Chapter 23
Slide 49
Project planning


Algorithmic cost models provide a basis for
project planning as they allow alternative
strategies to be compared
Embedded spacecraft system
•
•
•

Must be reliable
Must minimise weight (number of chips)
Multipliers on reliability and computer constraints > 1
Cost components
•
•
•
Target hardware
Development platform
Effort required
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 50
Management options
A. Us e exis ti ng hardware,
d evelo pmen t sy st em and
d ev elopm en t team
B . P ro ces so r an d
m em ory u pg rade
C . M em o ry
u pgrade on ly
Har dware co st in crease
Ex peri ence decrease
Hardw are co st
i ncrease
E. New de velo pm en t
s ys tem
F. S taff w it h
h ardware ex perien ce
D. M ore
ex perien ced st aff
Hardw are co st in crease
Ex peri ence decr ease
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 51
Management options costs
O ption
R E LY
S T OR
T IME
T O OL S
L T EX
A
1 .3 9
1 .0 6
1 .1 1
0 .8 6
1
63
B
1 .3 9
1
1
1 .1 2
1 .2 2
C
1 .3 9
1
1 .1 1
0 .8 6
D
1 .3 9
1 .0 6
1 .1 1
E
1 .3 9
1
F
1 .3 9
1
©Ian Sommerville 2000
T otal effor t S of tw a re co st
T otal co st
9 49 39 3
H ar dwa re
co st
1 00 00 0
88
1 31 35 50
1 20 00 0
1 40 20 25
1
60
8 95 65 3
1 05 00 0
1 00 06 53
0 .8 6
0 .8 4
51
7 69 00 8
1 00 00 0
8 97 49 0
1
0 .7 2
1 .2 2
56
8 44 42 5
2 20 00 0
1 04 41 59
1
1 .1 2
0 .8 4
57
8 51 18 0
1 20 00 0
1 00 27 06
Software Engineering, 6th edition. Chapter 23
1 04 93 93
Slide 52
Option choice

Option D (use more experienced staff) appears to
be the best alternative
•


However, it has a high associated risk as experienced staff may
be difficult to find
Option C (upgrade memory) has a lower cost
saving but very low risk
Overall, the model reveals the importance of staff
experience in software development
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 53
Project duration and staffing



As well as effort estimation, managers must
estimate the calendar time required to complete a
project and when staff will be required
Calendar time can be estimated using a
COCOMO 2 formula
•
TDEV = 3  (PM)(0.33+0.2*(B-1.01))
•
PM is the effort computation and B is the exponent computed as
discussed above (B is 1 for the early prototyping model). This
computation predicts the nominal schedule for the project
The time required is independent of the number
of people working on the project
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 54
Staffing requirements




Staff required can’t be computed by diving the
development time by the required schedule
The number of people working on a project varies
depending on the phase of the project
The more people who work on the project, the
more total effort is usually required
A very rapid build-up of people often correlates
with schedule slippage
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 55
Key points



Factors affecting productivity include individual
aptitude, domain experience, the development
project, the project size, tool support and the
working environment
Different techniques of cost estimation should be
used when estimating costs
Software may be priced to gain a contract and the
functionality adjusted to the price
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 56
Key points




Algorithmic cost estimation is difficult because of
the need to estimate attributes of the finished
product
The COCOMO model takes project, product,
personnel and hardware attributes into account when
predicting effort required
Algorithmic cost models support quantitative option
analysis
The time to complete a project is not proportional to
the number of people working on the project
©Ian Sommerville 2000
Software Engineering, 6th edition. Chapter 23
Slide 57
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Software cost estimation - University of Illinois at Chicago