University of Southern California
Center for Systems and Software Engineering
Cost Modeling for Commercial
Organizations
Anandi Hira, USC Graduate Student
COCOMO Forum
Thursday, November 3, 2011
University of Southern California
Center for Systems and Software Engineering
Outline
• Cost modeling challenges for large commercial
organizations
• Example of sizing challenges: Fidelity
• Candidate requirements-level approach: Cockburn
– IBM data
– eServices data
– WellPoint data
• Next steps
November 4, 2011
©USC-CSSE
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University of Southern California
Center for Systems and Software Engineering
Cost Modeling Challenges for Large
Commercial Organizations
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Numerous potential cost drivers
Diversity of application combinations
Reuse, NDI, and interoperability uncertainties
Diversity of collected application data
Need for early estimates
– Sizing based on variable-level requirements
– With variable requirements-to-code expansion factors
November 4, 2011
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University of Southern California
Center for Systems and Software Engineering
Potential Cost Driver Metadata: WellPoint
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Business Area (Health Solutions, Mandates)
Sponsoring Division (Finance, Human Resources)
Operational Capability (Care Mgmt., Claims Mgmt.)
Business Capability (Marketing, Enrollment)
Need for New Features (Data, Business Processes)
Primary Benefits (Higher Retention, Cost Avoidance)
Systems Impacted (eBusiness Portals, Call Centers)
States Impacted (California, New Hampshire)
Business Impact (Actuarial, Legal)
Estimated Size (<$1M, >$5M)
November 4, 2011
©USC-CSSE
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University of Southern California
Center for Systems and Software Engineering
November 4, 2011
Sizing Challenge: Fidelity
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University of Southern California
Center for Systems and Software Engineering
Reasons for Size Growth
• Sized just for U.S. part of company
– Actually a global company with many different data items,
required reports, and laws to comply with
– WellPoint similar with many state data items, reports, laws
• Sized as a computer program and not a system product
– Missed product functions such as data validation, user
assistance, and required security and privacy
– Missed system functions such as interoperability with non-US
systems and maintenance and diagnostic functions
– Brooks’ Mythical Man Month cites a factor of 3 each to go from a
computer program to a program product and then a program
system product, or an overall factor of 9
– Early WellPoint estimates may have similar size growth
November 4, 2011
©USC-CSSE
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University of Southern California
Center for Systems and Software Engineering
Outline
• Cost modeling challenges for large commercial
organizations
• Example of sizing challenges: Fidelity
• Candidate requirements-level approach: Cockburn
– IBM data
– eServices data
– WellPoint data
• Next steps
November 4, 2011
©USC-CSSE
7
University of Southern California
Center for Systems and Software Engineering
Requirement Levels Metaphor: Cockburn
November 4, 2011
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University of Southern California
Center for Systems and Software Engineering
IBM-UK Expansion Factor Experience
Business Objectives
5
Cloud
Business Events/Subsystems
35
Kite
Use Cases/Components
250
Sea level
Main Steps/Main Operations
2000
Fish
Alt. Steps/Detailed Operations
15,000
Clam
1,000K – 1,500K
Lava
SLOC*
*(70 – 100 SLOC/Detailed Operation)
(Hopkins & Jenkins, Eating the IT Elephant, 2008)
November 4, 2011
©USC-CSSE
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University of Southern California
Center for Systems and Software Engineering
Requirement Levels Ratio Study
Ali Malik, 2009
• 25 USC CSCI 577a,b projects
– Real-client e-services applications
– Similarities and Differences compared to
industry projects
– Complete information on all requirement
levels
November 4, 2011
©USC-CSSE
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University of Southern California
Center for Systems and Software Engineering
Summary of Elaboration Results
Elaboration Factors
Statistic
Average
2.46
Kite to
Sea
Level
0.89
Median
2.75
0.89
6.57
40.72
Standard 0.94
Deviation
0.55
2.97
64.46
November 4, 2011
Cloud to
Kite
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Sea
Level to
Fish
7.06
Fish to
Clam
66.91
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University of Southern California
Center for Systems and Software Engineering
Outline
• Cost modeling challenges for large commercial
organizations
• Example of sizing challenges: Fidelity
• Candidate requirements-level approach: Cockburn
– IBM data
– eServices data
– WellPoint data
• Next steps
November 4, 2011
©USC-CSSE
12
University of Southern California
Center for Systems and Software Engineering
WellPoint Data to Date
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3 New Projects with Requirements Levels, Metadata
Business Area (Health Solutions, Mandates)
Sponsoring Division (Finance, Human Resources)
Operational Capability (Care Mgmt., Claims Mgmt.)
Business Capability (Marketing, Enrollment)
Need for New Features (Data, Business Processes)
Primary Benefits (Higher Retention, Cost Avoidance)
Systems Impacted (eBusiness Portals, Call Centers)
States Impacted (California, New Hampshire)
Business Impact (Actuarial, Legal)
Estimated Size (<$1M, >$5M)
•
4 Finished Projects with Effort Data, Some
Requirements Levels, TBD Metadata
November 4, 2011
©USC-CSSE
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University of Southern California
Center for Systems and Software Engineering
WellPoint Requirements Level Data
IB
M
WP WP
P# P#2
1
Cloud to 7.0 2.0 31.0
Kite
Kite to
7.1 2.5 1.097
Sea
Level
November 4, 2011
WP WP
P#3 FP#1
4.5
WP
WP
FP#2 FP
#3
12.375 3.625 3
2.6
1.175
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TBD
WP
FP
#4
17.3
0.97 TBD
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University of Southern California
Center for Systems and Software Engineering
Finished Projects Data and Observations
Total
Hours
FP #1
51,584
FP #2
FP #3 FP #4
1,7240.5 3,935.5 7,050.5
Hours/Clo 2,149.3 1,077.53 135.71
ud
Hours/Kite 173.68 297.25
45.236
Hours/Se 147.81 TBD
46.851
a Level
November 4, 2011
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1,007.2
58.269
TBD
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University of Southern California
Center for Systems and Software Engineering
Outline
• Cost modeling challenges for large commercial
organizations
• Example of sizing challenges: Fidelity
• Candidate requirements-level approach: Cockburn
– IBM data
– eServices data
– WellPoint data
• Next steps
November 4, 2011
©USC-CSSE
16
University of Southern California
Center for Systems and Software Engineering
Next Steps
• Discuss project data similarities, anomalies
– Identify additional sources of explanatory data
• Obtain additional project data where
possible
– Function point counts
– Requirements level clarifications
– Partial metadata
• Obtain data from additional projects
November 4, 2011
©USC-CSSE
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