SW_Cost_Estimation.ppt

Software Engineering SW Cost Estimation Slide 1
Software Engineering
Software Cost Estimation

Objectives
 To introduce the fundamentals of software costing
and pricing
 To explain software productivity metric
 To explain why different techniques for software
estimation:
LOC model
Function points model
Object point model
COCOMO (COnstructive COst MOdel): 2 algorithmic cost
estimation model
UCP: Use Case Points

What is Software Cost Estimation
 Predicting the cost of resources
required for a software
development process

Software is a Risky Business
70% Completed
30% Not completed
 53% of projects cost almost 200% of original estimate.
 Estimated $81 billion spent on failed U.S. projects in
1995.
All surveyed projects used waterfall lifecycle.

Software is a Risky Business
 British Computer Society (BCS) survey:
1027 projects
Only 130 were successful !
 Success was defined as:
deliver all system requirements
within budget
within time
to the quality agreed on

Why early Cost Estimation?
 Cost estimation is needed early for s/w pricing
 S/W price = cost + profit

Fundamental estimation questions
 Effort
How much effort is required to complete an activity?
Units: man-day (person-day), man-week, man-month,,..
 Duration
How much calendar time is needed to complete an activity? Resources
assigned
Units: hour, day, week, month, year,..
 Cost of an activity
What is the total cost of an activity?
 Project estimation and scheduling are interleaved
management activities

Software Cost Components
1. Effort costs (dominant factor in most projects)
salaries
Social and insurance & benefits
2. Tools costs: Hardware and software for development
Depreciation on relatively small # of years 300K US$
3. Travel and Training costs (for particular client)
4. Overheads(OH): Costs must take overheads into account
costs of building, air-conditioning, heating, lighting
costs of networking and communications (tel, fax, )
costs of shared facilities (e.g library, staff restaurant, etc.)
depreciation costs of assets
Activity Based Costing (ABC)

S/W Pricing Policy
S/W price is influenced by
 economic consideration
 political consideration
 and business consideration

Software Pricing Policy/Factors
Factor Description
Market opportunity A development organisation may quote a low price
because it wishes to move into a new segment of the
software market. Accepting a low profit on one
project may give the opportunity of more profit later.
The ex
perience gained may allow new products to be
developed.
Cost estimate uncertainty If an organisation is unsure of its cost estimate, it
may increase its price by some contingency over and
above its normalprofit.
Contractual terms A customer may be willing to allow the developer to
retain ownership of the source code and reuse it in
other projects. The price charged may then be less
than if the software source code is handed over to the
customer.
Requirements volatility If the requirements are likely to change, an
organisation may lower its price to win a contract.
After the contract is awarded, high prices may be
charged for changes to the requirements.
Financialhealth Developers in financial difficulty may lower their
price to gain a contract. It is better to make a small
profit or break even than to go out of business.

 Rate of s/w production
Needs for measurements
Measure software produced per time unit (Ex: LOC/hr)
rate of s/w production
software produced including documentation
 Not quality-oriented: although quality assurance
is a factor in productivity assessment
Programmer Productivity

S/W productivity measures are based on:
 Size related measures:
Based on some output from the software process
Number lines of delivered source code (LOC)
 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)
Object-points
UCP
Productivity measures

 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
Measurement problems

 Program length (LOC) can be used to predict program
characteristics e.g. person-month effort and ease of
maintenance
 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
Lines Of Code (LOC)

Versions of LOC
 DSI : Delivered Source Instructions
 KLOC Thousands of LOC
 DSI
One instruction is one LOC
Declarations are counted
Comments are not counted

LOC
 Advantages
Simple to measure
 Disadvantages
Defined on code: it can not measure the size of specification
Based on one specific view of size: length.. What about complexity and
functionality !!
Bad s/w may yield more LOC
Language dependent
 Therefore: Other s/w size attributes must be included

 The lower level the language, the less
productive the programmer
The same functionality takes more code to implement in a
lower-level language than in a high-level language
 Measures of productivity based on LOC suggest
that programmers who write verbose code are
more productive than programmers who write
compact code !!!
LOC Productivity

Function Points: FP
Function Points is used in 2 contexts:
 Past: To develop metrics from historical data
 Future: Use of available metrics to size the s/w of
a new project

Function Points
 Based on a combination of program characteristics
 The number of :
External (user) inputs: input transactions that update internal files
External (user) outputs: reports, error messages
User interactions: inquiries
Logical internal files used by the system:
Example a purchase order logical file composed of 2 physical
files/tables Purchase_Order and Purchase_Order_Item
External interfaces: files shared with other systems
 A weight (ranging from 3 for simple to 15 for complex
features) is associated with each of these above
 The function point count is computed by multiplying each
raw count by the weight and summing all values

Function Points - Calculation
complexity multiplier
function points
number of user inputs
number of user outputs
number of user inquiries
number of files
number of ext.interfaces
measurement parameter
3
4
3
7
5
count
weighting factor
simple avg. complex
4
5
4
10
7
6
7
6
15
10
=
=
=
=
=
count-total
X
X
X
X
X

Function Points – Taking Complexity
into Account -14 Factors Fi
Each factor is rated on a scale of:
Zero: not important or not applicable
Five: absolutely essential
1. Backup and recovery
2. Data communication
3. Distributed processing functions
4. Is performance critical?
5. Existing operating environment
6. On-line data entry
7. Input transaction built over multiple screens

into Account -14 Factors Fi (cont.)
8. Master files updated on-line
9. Complexity of inputs, outputs, files, inquiries
10. Complexity of processing
11. Code design for re-use
12. Are conversion/installation included in design?
13. Multiple installations
14. Application designed to facilitate change by the
user

into Account -14 Factors Fi (cont.)
FP = UFC * [ 0.65 + 0.01 * F ]
UFC: Unadjusted function point count
0 <= F <= 5
i
i=1
i=14
i

FP: Advantages & Disadvantages
 Advantages
Available early .. We need only a detailed specification
Not restricted to code
Language independent
More accurate than LOC
 Disadvantages
Ignores quality issues of output
Subjective counting .. depend on the estimator
Hard to automate.. Automatic function-point counting is
impossible

Function points and LOC
 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
approximately 300 for assemble language to 12-40 for a 4GL

Relation Between FP & LOC
Programming Language LOC/FP (average)
Assembly language 320
C 128
COBOL 106
FORTRAN 106
Pascal 90
C++ 64
Ada 53
Visual Basic 32
Smalltalk 22
Power Builder (code generator) 16
SQL 12

Function Points & Normalisation
 Function points are used to normalise measures (same as
for LOC) for:
S/w productivity
Quality
 Error (bugs) per FP (discovered at programming)
 Defects per FP (discovered after programming)
 $ per FP
 Pages of documentation per FP
 FP per person-month

Expected Software Size
 Based on three-point
 Compute Expected Software Size (S) as weighted
average of:
Optimistic estimate: S(opt)
Most likely estimate: S(ml)
Pessimistic estimate: S(pess)
 S = { S(opt) + 4 S(ml) + S(pess) } / 6
Beta probability distribution

Example 1: LOC Approach
• A system is composed of 7 subsystems as below.
• Given for each subsystem the size in LOC and the
2 metrics: productivity LOC/pm (pm: person month) ,Cost $/LOC
• Calculate the system total cost in $ and effort in months .

Functions
UICF
2DGA
3DGA
DSM
CGDF
PCF
DAM
Totals
estimated LOC $/LOC Cost Effort (months)
LOC/pm
2340
5380
6800
3350
4950
2140
8400
33,360
14
20
20
18
22
28
18
315
220
220
240
200
140
300
32,000
107,000
136,000
60,000
109,000
60,000
151,000
655,000
7.4
24.4
30.9
13.9
24.7
15.2
28.0
145.0

Assuming
 Estimated project LOC = 33200
 Organisational productivity (similar project type) = 620
LOC/p-m
 Burdened labour rate = 8000 $/p-m
Then
 Effort = 33200/620 = (53.6) = 54 p-m
 Cost per LOC = 8000/620 = (12.9) = 13 $/LOC
 Project total Cost = 8000 * 54 = 432000 $

Example 3: FP Approach

Example 3: FP Approach (cont.)
Complexity Factor

Assuming F = 52
FP = UFC * [ 0.65 + 0.01 * F ]
FP = 342 * 1.17 = 400
Complexity adjustment factor = 1.17
i
i
i
i

Assuming
 Estimated FP = 401
 Organisation average productivity (similar project type) =
6.5 FP/p-m (person-month)
 Burdened labour rate = 8000 $/p-m
Then
 Estimated effort = 401/6.5 = (61.65) = 62 p-m
 Cost per FP = 8000/6.5 = 1231 $/FP
 Project cost = 8000 * 62 = 496000 $

Object Points (for 4GLs)
 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
C:Software_EngCocomoSoftware Measurement Page, COCOMO II,
object points.htm

Object Points – Weighting

Object Points – Weighting (cont.)
 srvr: number of server data tables used with screen/report
 clnt: number of client data tables used with screen/report

Object Point Estimation
 Object points are easier to estimate from a
specification than function points
simply concerned with screens, reports and 3GL modules
 At an early point in the development process:
Object points can be easily estimated
It is very difficult to estimate the number of lines of code in a
system

 LOC productivity
Real-time embedded systems, 40-160 LOC/P-month
Systems programs , 150-400 LOC/P-month
Commercial applications, 200-800 LOC/P-month
 Object points productivity
measured 4 - 50 object points/person-month
depends on tool support and developer capability
Productivity Estimates

Object Point Effort Estimation
 Effort in p-m = NOP / PROD
NOP = number of OP of the system
Example: An application contains 840 OP
(NOP=840) & Productivity is very high (= 50)
Then, Effort = 840/50 = (16.8) = 17 p-m

Adjustment for % of Reuse
Adjusted NOP = NOP * (1 - % reuse / 100)
Example:
An application contains 840 OP, of which 20% can be
supplied by existing components.
Adjusted NOP = 840 * (1 – 20/100) = 672 OP
Adjusted effort = 672/50 = (13.4) = 14 p-m

Factors affecting productivity
Factor Description
Application domain
ex
perience
Knowledge of the application domain is essential for
effective software development. Engineers whoalready
understand a domain are likely to be the most
productive.
Process quality The development process used can have a significant
effect on productivity. This is covered in Chapter 31.
Project size The larger a project, the more time required for team
communications. Less time is available for
development so individualproductivity is reduced.
Technology support Good support technology such as CASEtools,
supportive configuration management systems, etc.
can improve productivity.
Working environment As discussed in Chapter 28, a quiet working
environment with private work areas contributes to
improved 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
 If change is constant, then an approach based on
counting lines of code (LOC) is not meaningful
Quality and Productivity

Estimation techniques
 There is no simple way to make an accurate estimate of
the effort required to develop a software system:
Initial estimates may be 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

Estimation techniques
 Algorithmic cost modelling
 Expert judgement
 Estimation by analogy
 Parkinson's Law
 Pricing to win

Algorithmic code modelling
 A formula – empirical relation:
based on historical cost information and which is generally
based on the size of the software
 The formulae used in a formal model arise from
the analysis of historical data.

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!

Estimation by Analogy
 Experience-based Estimates
 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

Estimation by Analogy :
Problems
 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

Parkinson's Law
 “The project costs whatever resources are available”
(Resources are defined by the software house)
 Advantages: No overspend
 Disadvantages: System is usually unfinished
The work is contracted to fit the budget available: by reducing
functionality, quality

Pricing to Win
 The project costs whatever the customer budget is.
 Advantages: You get the contract
 Disadvantages:
The probability that the customer gets the system he/she wants is
small.
Costs do not accurately reflect the work required

Pricing to Win
 This approach may seem unethical and unbusiness like
 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

Top-down and Bottom-up Estimation
 Top-down
Start at the system level and assess the overall system
functionality
 Bottom-up
Start at the component level and estimate the effort required for
each component. Add these efforts to reach a final estimate

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

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

Estimation Methods
 S/W project 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

Algorithmic Cost Modelling
 Most of the work in the cost estimation field has focused on
algorithmic cost modelling.
 Costs are analysed using mathematical formulas linking costs or
inputs with METRICS to produce an estimated output.
 The formula is based on the analysis of historical data.
 The accuracy of the model can be improved by calibrating the model
to your specific development environment, (which basically involves
adjusting the weighting parameters of the metrics).

Building Metrics from
measurements
Project n
Project 2 Historical Data
.
.
.
.
METRICS
Analysis of
historical data
Project 1

New Project estimation using
available Metrics
METRICS
New Project
Estimates for new project

Empirical Estimation Models -
effort = tuning coefficient * size
exponent
usually derived
as person-months
of effort required
either an organisation-dependent constant or
a number derived based on complexity of project
usually LOC but
may also be
function point
empirically
derived

Effort = A  SizeB  M
A is an organisation-dependent constant
B reflects the nonlinearity (disproportionate) effort for large projects
M is a multiplier reflecting product, process and people attributes
 Most commonly used product attribute for cost
estimation is code size (LOC)
 Most models are basically similar but with
different values for A, B and M

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

Estimate Uncertainty
x
2x
4x
0.5x
0.25x
Feasibility Requirements Design Code
Delivery
Higher uncertainty
Lower uncertainty
measurements

The COCOMO Cost model
Constructive Cost Model
 An empirical model based on project experience
 COCOMO'81 is derived from the analysis of 63
software projects in 1981.
 Well-documented, ‘independent’ model which is
not tied to a specific software vendor
 COCOMO II (2000) takes into account different
approaches to software development, reuse, etc.

COCOMO 81
M: multiplier similar as for COCOMO II , based on 15 cost drivers
KDSI: Thousands of Delivered Source Instructions (KLOC)
Project
complexity
Formula Description
Simple
(Organic)
PM = 2.4 (KDSI)1.05  M Well-understood applications
developed by small teams.
Moderate
(Semi-
detached)
PM = 3.0 (KDSI)1.12  M More complex projects where
team members may have limited
experience of related systems.
Embedded PM = 3.6 (KDSI)1.20  M Complex projects where the
software is part of a strongly
coupled complex of hardware,
software, regulations and
operational procedures.

Metrics: Parameters calculations
 Least Squares method – Curve fitting
 Given: n measurements of pairs (xi, yi)
 Required: Best fit of measurements to get metrics parameters
 Assume: A linear relation between measured pairs:
Y = a + b x
Other relations may be assumed as quadratic ‘or higher’:
Y = a + b x + c x*x , …
 Get metrics parameters a, b that best fit the measurements

How to get parameters a, b
Measured Pair (xi, yi)
Fitting Error ei = Yi - yi
Measured xi
Measured yi
Fitted Yi
Fitting Line Yi = a + b*xi

 ei = Yi – yi = a + b*xi - yi
 For all measurements get S as:
S is the sum mover n measurements of squared
values of ei
 S = Σ (ei) = Σ (a + b*xi - yi)
 S = S(a, b)
2
2

 Best fitting when S is minimum
 S is minimum when both the partial derivatives of
S with respect to a and b are zero.
 This leads to 2 equations in a and b.
 Solve and get a and b.

COCOMO II
 COCOMO II is a 3-level model that allows increasingly
detailed estimates to be prepared as development progresses
Early prototyping level
Estimates based on object points and a simple formula is used for effort estimation
Early design level
Estimates based on function points that are then translated to LOC
Includes 7 cost drivers
Post-architecture level
Estimates based on lines of source code or function point
Includes 17 cost drivers
 Five scale factors replace COCOMO 81 ratings (organic,
semi-detached, and embedded)

Early prototyping level - COCOMO II
 Suitable for projects built using modern GUI-builder tools
Based on Object Points
 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

Early Design Level: 7 cost
drivers - COCOMO II
 Estimates can be made after the requirements have been agreed
 Based on standard formula for algorithmic models
PM = A  SizeB  M + PMm
M = PERS  RCPX  RUSE  PDIF  PREX  FCIL  SCED
A = 2.5 in initial calibration,
Size: manually developed code in KLOC
Exponent B
• varies from 1.1 to 1.24 depending on novelty of the project,
development flexibility, risk management approaches and the
process maturity.
• B is calculated using a Scale Factor based on 5 exponent drivers
PMm: represents manual adaptation for automatically generated code
Effort for Manual adaptation
of Automatically generated
code
Effort for Manually developed code

PMm : Manual Adaptation for
Automatically Generated Code ..
PMm = (ASLOC  (AT/100)) / ATPROD
 Used when big % of code is generated automatically
 ASLOC :Size of adapted components
 ATPROD: Productivity of the engineer integrating the
adapted code (app. 2400 source statements per month)
 AT: % of adapted code (that is automatically generated)

COCOMO II Early Design Stage
Effort Multipliers: 7 cost drivers
 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

The Exponent B
Scale Factor(SF) - COCOMO II
 Exponent B for effort calculation
 B = 1.01 + 0.01 x sum [SF (i)] , i=1,…, 5
SF = Scale Factor
 Each SF is rated on 6-point scale (ranging from 0 to 5) :
very low (5), low ( 4), nominal (3), high (2), very high (1), extra high (0)
 5 Scale Factor (exponent drivers)
Precedenteness
Development flexibility
Architecture/risk resolution
Team cohesion
Process maturity
Ex: 20 KLOC ^ 1.26 / 20 KLOC ^ 1.01 = 43.58/20.6 = 2.11

Exponent scale factors - COCOMO II
Scale factor Explanation
Precedentedness Reflects the previous experience of the organisation
with this type of project. Very low means no previous
experience, Extra high means that the organisation is
completely famili ar with this applicationdomain.
Deve lopment
flexibility
Reflects the degree of flexib ilit y in the development
process. Very low means a prescribed process is used;
Extra high means that the client only sets gene ral goals.
Architecture/risk
resolution
Reflects the extent of risk analysis carried out. Very low
means littl e analysis, Extra high means a complete a
thorough risk analysis.
Team cohesion Reflects how well the development team know e ach
other and work together. Very low means very diffi cult
interactions, Extra high means an integrated and
effective team withno communicationproblems.
Process maturity Reflects the process maturityof the organisation. The
computationof this value depends on the CMM
Maturity Questionna ire but an estimate can be achieved
by subtracting the CMM process maturity level from 5.

Given:
 Precedenteness - new project – rated low SF(1) = 4
 Development flexibility - no client involvement – rated
Very high - SF(2) = 1
 Architecture/risk resolution - No risk analysis – rated Very
Low - SF(3) = 5
 Team cohesion - new team - nominal - SF(4) = 3
 Process maturity - some control - nominal - SF(5) = 3
Then:
 Exponent B =1.17
Example: Exponent B
calculations using Scale Factor

Post-architecture stage - COCOMO II
 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.

 Product attributes (5 multipliers)
concerned with required characteristics of the software product being
developed
 Computer attributes (3 multipliers)
constraints imposed on the software by the hardware platform
 Personnel attributes (6 multipliers)
multipliers that take the experience and capabilities of the people
working on the project into account.
 Project attributes (3 multipliers)
concerned with the particular characteristics of the software
development project
COCOMO II Post Architecture
Effort Multipliers (17 multipliers)

COCOMO II Post Architecture
Effort Multipliers:17 cost drivers
Product attributes
RELY Required system
reliability
DATA Size of database used
CPLX Complexity of system
modules
RUSE Required percentage of
reusable components
DOCU Extent of documentation
required
Computer attributes
TIME Execution time
constraints
STOR Memory constraints
PVOL Volatilit yof
development platform
Personnel attributes
ACAP Capability of project
analysts
PCAP Programmer capabilit y
PCON Personne l continuity AEXP Ana lyst experience inproject
domain
PEXP Programmer experience
inproject domain
LTEX Language and tool experience
Project attributes
TOOL Use of software tools SITE Extent of multi-site working
and qualit yof site
communications
SCED Deve lopment schedule
compression

Effects of cost drivers
Maximum & Minimum Data are from ref: Boehm, 1997
Exponent value 1.17
System size (including factors for reuse
and requirements volatility)
128, 000 DSI
Initial COCOMO estimate without
cost drivers (M=1)
730 person-months
Reliability Very high, multiplier = 1.39
Complexity Very high, multiplier = 1.3
Memory constraint High, multiplier = 1.21
Tool use Low, multiplier = 1.12
Schedule Accelerated, multiplier = 1.29
Adjusted COCOMO estimate: 2306 person-months
Reliability Very low, multiplier = 0.75
Complexity Very low, multiplier = 0.75
Memory constraint None, multiplier = 1
Tool use Very high, multiplier = 0.72
Schedule Normal, multiplier = 1
Adjusted COCOMO estimate: 295 person-months
Maximum
Minimum

Effects of cost drivers (M = ?)
Maximum & Minimum Data are from ref: Boehm, 1997
Exponent value 1.17
System size (including factors for reuse
and requirements volatility)
128, 000 DSI
Initial COCOMO estimate without
cost drivers (M=1)
730 person-months
Reliability Very high, multiplier = 1.39
Complexity Very high, multiplier = 1.3
Memory constraint High, multiplier = 1.21
Tool use Low, multiplier = 1.12
Schedule Accelerated, multiplier = 1.29
Adjusted COCOMO estimate:
M = Π (Mi ) = 3.15
2306 person-months
Reliability Very low, multiplier = 0.75
Complexity Very low, multiplier = 0.75
Memory constraint None, multiplier = 1
Tool use Very high, multiplier = 0.72
Schedule Normal, multiplier = 1
Adjusted COCOMO estimate:
M = Π (Mi ) = 0.405
295 person-months

 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
Project planning

Management options
A. Use existing hardware,
development system and
development team
C. Memory
upgrade only
Hardware cost
increase
B. Processor and
memory upgrade
Hardware cost increase
Experience decrease
D. More
experienced staff
F. Staff with
hardware experience
E. New development
system
Hardware cost increase
Experience decrease

Management options costs
Option RELY STOR TIME TOOLS LTEX Total effort Software cost Hardware
cost
Total cost
A 1.39 1.06 1.11 0.86 1 63 949393 100000 1049393
B 1.39 1 1 1.12 1.22 88 1313550 120000 1402025
C 1.39 1 1.11 0.86 1 60 895653 105000 1000653
D 1.39 1.06 1.11 0.86 0.84 51 769008 100000 897490
E 1.39 1 1 0.72 1.22 56 844425 220000 1044159
F 1.39 1 1 1.12 0.84 57 851180 120000 1002706

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

Project duration and staffing- COCOMO II
 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 II
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

Project duration and staffing
Example
 Given:
Software development effort = 60 PM
Exponent B = 1.17
 Then:
Nominal schedule for the project (calendar time TDEV required
to complete the project):
TDEV = 3  (PM)(0.33+0.2*(1.17-1.01))
= 3  (PM)(0.36)
= 13 months

Staffing requirements
 Staff required can’t be computed by diving the
development time by the required schedule – Non
linear relation ship
 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

Use Case Points UCP
 Effort: person-month based on Use Case
description.
 See file: Use_Case_Points.doc

SW_Cost_Estimation.ppt

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SW_Cost_Estimation.ppt