Software Cost
Estimation
Seth Bowen
Samuel Lee
Lance Titchkosky
Outline
 Introduction
 Inputs and Outputs
 Methods of Estimation
 COCOMO
 Conclusion
2
Cost Estimation Is Needed
 55% of projects over budget
 24 companies that developed large distributed
systems (1994)
 53% of projects cost 189% more than
initial estimates
 Standish Group of 8,380 projects (1994)
3
Cost Estimation
 An approximate judgment of the costs for a
project
 Many variables
 Often measured in terms of effort (i.e., person
months/years)
 Different development environments will
determine which variables are included in the
cost value
 Management costs
 Development costs
 Training costs
 Quality assurance
 Resources
4
Cost Estimation Affects
 Planning and budgeting
 Requirements prioritization
 Schedule
 Resource allocation
 Project management
 Personnel
 Tasks
5
Cost Estimation During the
Software Life Cycle
 Cost estimation should be done throughout the
software life cycle to allow for refinement
 Need effective monitoring and control of the
software costs to verify and improve accuracy of
estimates
 At appropriate level of detail
 Gathering data should not be difficult
 Success of a cost estimate method is not
necessarily the accuracy of the initial estimates,
but rather the rate at which estimates converge
to the actual cost
6
Who is the Estimator?
 Someone responsible for the implementation
 Can compare previous projects in organization to
current project
 Usually experienced
 Someone from outside the organization
 Can provide unbiased estimate
 Tend to use empirical methods of estimation
 Difficulties:
 Lack of confidence that a model will outperform an expert
 Lack of historical data to calibrate the model
7
General Steps and Remarks
 Establish Plan
 What data should we gather
 Why are we gathering this data
 What do we hope to accomplish
 Do cost estimation for initial requirements
 Decomposition
 Use several methods
 There is no perfect technique
 If get wide variances in methods, then should reevaluate the information used to make estimates
8
General Steps and Remarks (cont.)
 Do re-estimates during life cycle
 Make any required changes to
development
 Do a final assessment of cost estimation
at the end of the project
9
Software Cost Estimation
Process
 Definition
 A set of techniques and procedures that is
used to derive the software cost estimate
 Set of inputs to the process and then the
process will use these inputs to generate
the output
10
Inputs and Outputs to the
Estimation Process
Classical view of software estimation process [Vigder/Kark]
11
Inputs and Outputs to the
Estimation Process (Cont.)
Actual cost estimation process [Vigder/Kark]
12
Cost Estimation Accuracy
 To determine how well a cost estimation
model predicts
 Assessing model performance
 Absolute Error = (Epred – Eact)
 Percentage Error = (Epred – Eact) / Eact
 Mean Magnitude of Relative Error
 E pred  E act 


i
.

n i  1 
E act
1
in
13
Cost Estimation Methods






Algorithmic (Parametric) model
Expert Judgment (Expertise Based)
Top – Down
Bottom – Up
Estimation by Analogy
Price to Win Estimation
14
Algorithmic (Parametric) Model
 Use of mathematical equations to perform
software estimation
 Equations are based on theory or historical data
 Use input such as SLOC, number of functions to
perform and other cost drivers
 Accuracy of model can be improved by
calibrating the model to the specific environment
15
Algorithmic (Parametric) Model
(Cont.)
 Examples:
 COCOMO (COnstructive COst MOdel)
 Developed by Boehm in 1981
 Became one of the most popular and most transparent cost
model
 Mathematical model based on the data from 63 historical
software project
 COCOMO II
 Published in 1995
 To address issue on non-sequential and rapid development
process models, reengineering, reuse driven approaches,
object oriented approach etc
 Has three submodels – application composition, early design
and post-architecture
16
Algorithmic (Parametric) Model
(Cont.)
 Putnam’s software life-cycle model (SLIM)
 Developed in the late 1970s
 Based on the Putnam’s analysis of the life-cycle in
terms of a so-called Rayleigh distribution of project
personnel level versus time.
 Quantitative software management developed
three tools : SLIM-Estimate, SLIM-Control and
SLIM-Metrics.
17
Algorithmic (Parametric) Model
(Cont.)
 Advantages




Generate repeatable estimations
Easy to modify input data
Easy to refine and customize formulas
Objectively calibrated to experience
 Disadvantages
 Unable to deal with exceptional conditions
 Some experience and factors can not be quantified
 Sometimes algorithms may be proprietary
18
Expert Judgment
 Capture the knowledge and experience of the
practitioners and providing estimates based
upon all the projects to which the expert
participated.
 Examples
 Delphi
 Developed by Rand Corporation in 1940 where participants
are involved in two assessment rounds.
 Work Breakdown Structure (WBS)
 A way of organizing project element into a hierarchy that
simplifies the task of budget estimation and control
19
Expert Judgment (Cont.)
 Advantages
 Useful in the absence of quantified, empirical data.
 Can factor in differences between past project
experiences and requirements of the proposed
project
 Can factor in impacts caused by new technologies,
applications and languages.
 Disadvantages
 Estimate is only as good expert’s opinion
 Hard to document the factors used by the experts
20
Top - Down
 Also called Macro Model
 Derived from the global properties of the
product and then partitioned into various
low level components
 Example – Putnam model
21
Top – Down (Cont.)
 Advantages
 Requires minimal project detail
 Usually faster and easier to implement
 Focus on system level activities
 Disadvantages
 Tend to overlook low level components
 No detailed basis
22
Bottom - Up
 Cost of each software components is
estimated and then combine the results to
arrive the total cost for the project
 The goal is to construct the estimate of the
system from the knowledge accumulated
about the small software components and
their interactions
 An example – COCOMO’s detailed model
23
Bottom – Up (Cont.)
 Advantages
 More stable
 More detailed
 Allow each software group to hand an
estimate
 Disadvantages
 May overlook system level costs
 More time consuming
24
Estimation by Analogy
 Comparing the proposed project to
previously completed similar project in the
same application domain
 Actual data from the completed projects
are extrapolated
 Can be used either at system or
component level
25
Estimation by Analogy (Cont.)
 Advantages
 Based on actual project data
 Disadvantages
 Impossible if no comparable project had been
tackled in the past.
 How well does the previous project represent
this one
26
Price to Win Estimation
 Price believed necessary to win the
contract
 Advantages
 Often rewarded with the contract
 Disadvantages
 Time and money run out before the job is
done
27
COCOMO 81
 COCOMO stands for COnstructive
COst MOdel
 It is an open system First published by
Dr Barry Bohem in 1981
 Worked quite well for projects in the
80’s and early 90’s
 Could estimate results within ~20% of
the actual values 68% of the time
28
COCOMO 81
 COCOMO has three different models (each one
increasing with detail and accuracy):
 Basic, applied early in a project
 Intermediate, applied after requirements are specified.
 Advanced, applied after design is complete
 COCOMO has three different modes:
 Organic – “relatively small software teams develop
software in a highly familiar, in-house environment”
[Bohem]
 Embedded – operate within tight constraints, product is
strongly tied to “complex of hardware, software,
regulations, and operational procedures” [Bohem]
 Semi-detached – intermediate stage somewhere
between organic and embedded. Usually up to 300
KDSI
29
COCOMO 81
 COCOMO uses two equations to calculate effort in man months
(MM) and the number on months estimated for project (TDEV)
 MM is based on the number of thousand lines of delivered
instructions/source (KDSI)
 MM = a(KDSI)b * EAF
 TDEV = c(MM)d
 EAF is the Effort Adjustment Factor derived from the Cost
Drivers, EAF for the basic model is 1
 The values for a, b, c, and d differ depending on which mode
you are using
Mode
a
b
c
d
Organic
2.4
1.05
2.5
0.38
Semi-detached
3.0
1.12
2.5
0.35
Embedded
3.6
1.20
2.5
0.32
30
COCOMO 81
 A simple example:
Project is a flight control system (mission critical) with
310,000 DSI in embedded mode
 Reliability must be very high (RELY=1.40). So we
can calculate:
 Effort = 1.40*3.6*(319)1.20 = 5093 MM
 Schedule = 2.5*(5093)0.32 = 38.4 months
 Average Staffing = 5093 MM/38.4 months = 133
FSP
31
COCOMO II
 Main objectives of COCOMO II:
 To develop a software cost and schedule
estimation model tuned to the life cycle
practices of the 1990’s and 2000’s
 To develop software cost database and tool
support capabilities for continuous model
improvement
 From “Cost Models for Future Software Life Cycle Processes:
COCOMO 2.0," Annals of Software Engineering, (1995).
32
COCOMO II
Has three different models:
 The Application Composition Model
 Good for projects built using rapid application
development tools (GUI-builders etc)
 The Early Design Model
 This model can get rough estimates before the entire
architecture has been decided
 The Post-Architecture Model
 Most detailed model, used after overall architecture
has been decided on
33
COCOMO II Differences
 The exponent value b in the effort equation is
replaced with a variable value based on five
scale factors rather then constants
 Size of project can be listed as object points,
function points or source lines of code (SLOC).
 EAF is calculated from seventeen cost drivers
better suited for today's methods, COCOMO81
has fifteen
 A breakage rating has been added to address
volatility of system
34
COCOMO II Calibration
 For COCOMO II results to be accurate the model
must be calibrated
 Calibration requires that all cost driver parameters be
adjusted
 Requires lots of data, usually more then one
company has
 The plan was to release calibrations each year but so
far only two calibrations have been done (II.1997,
II.1998)
 Users can submit data from their own projects to be
used in future calibrations
35
Importance of Calibration
 Proper calibration is very important
 The original COCOMO II.1997 could
estimate within 20% of the actual values
46% of the time. This was based on 83
data points.
 The recalibration for COCOMO II.1998
could estimate within 30% of the actual
values 75% of the time. This was based
on 161 data points.
36
Is COCOMO the Best?
 COCOMO is the most popular method however
for any software cost estimation you should
really use more then one method
 Best to use another method that differs
significantly from COCOMO so your project is
examined from more then one angle
 Even companies that sell COCOMO based
products recommend using more then one
method. Softstar (creators of Costar) will even
provide you with contact information for their
competitor’s products
37
COCOMO Conclusions
 COCOMO is the most popular software
cost estimation method
 Easy to do, small estimates can be done
by hand
 USC has a free graphical version available
for download
 Many different commercial version based
on COCOMO – they supply support and
more data, but at a price
38
Conclusions
 Project costs are being poorly estimated
 The accuracy of cost estimation has to be
improved
 Data collection
 Use of tools
 Use several methods of estimation
39
References
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Boehm B., Clark B., Horowitz E., Madachy R., Shelby R., Westland C.
(1995). Cost Models for Future Software Life Cycle Processes: COCOMO
2.0, Annals of Software Engineering.
http://sunset.usc.edu/research/COCOMOII/Docs/stc.pdf.
Boehm B., Clark B., Horowitz E., Madachy R., Shelby R., Westland C.
(1995). An Overview of the COCOMO 2.0 Software Cost Model.
http://sunset.usc.edu/research/COCOMOII/Docs/stc.pdf.
Boehm B., Chulani S., Clark B. (1997). Calibration Results of COCOMO
II.1997. http://sunset.usc.edu/publications/TECHRPTS/1998/usccse98502/CalPostArch.pdf.
Boehm B., Chulani S., Clark B. (1997). Calibrating the COCOMO II Post
Architecture Model.
http://sunset.usc.edu/Research_Group/Sunita/down/calpap.pdf.
Boehm B., Chulani S., Reifer D., The Rosetta Stone: Making COCOMO 81
Files Work With COCOMO II.
http://sunset.usc.edu/publications/TECHRPTS/1998/usccse98516/usccse98-516.pdf.
Chulani, S. (1998). Software Development Cost Estimation Approaches – A
Survey. IBM Research.
Humphrey, W.S. (1990). Managing the Software Process. Addison-Wesley
Publishing Company, New York, NY.
40
References
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Hussein, A. (2001). Introduction to Software Process Management.
University of Calgary, Calgary, Canada.
http://sern.ucalgary.ca/courses/SENG/621/W01/intro.ppt.
Londeix, B. (1987). Cost Estimation for Software Development. AddisonWesley Publishing Company, New York, NY.
Pressman, R.S. (2001). Software Engineering: A Practitioner’s Approach.
McGraw-Hill Higher Education, New York, NY.
Vigder, M. R. and Kark, A. W. (1994). Software Cost Estimation and Control.
Software Engineering Institute for Information Technology.
http://wwwsel.iit.nrc.ca/seldocs/cpdocs/NRC37116.pdf.
Wu, L. (1997). The comparison of the Software Cost Estimating Methods.
University of Calgary, Calgary, Canada.
http://sern.ucalgary.ca/courses/seng/621/W97/wul/seng621_11.html.
Basic COCOMO Software Cost Model.
http://www.jsc.nasa.gov/bu2/COCOMO.html.
COCOMO 2, Softstar Systems.
http://www.softstarsystems.com/cocomo2.htm.
Answers to Frequently Asked Questions, Softstar Systems.
http://www.softstarsystems.com/faq.htm.
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Questions and Discussion
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