University of Southern California
Center for Systems and Software Engineering
Integrating Case-Based, Analogy-Based,
and Parameter-Based Estimation via
Agile COCOMO II
Anandi Hira, USC Graduate Student
COCOMO Forum 2012
Wednesday, October 17, 2012
© USC-CSSE
1
University of Southern California
Center for Systems and Software Engineering
Outline
• Motivation
• Nature of Agile COCOMO II
• Extensions to Case-Based Reasoning
• Example of Use: WellPoint
• Further Potential Extensions
© USC-CSSE
2
University of Southern California
Center for Systems and Software Engineering
Motivation
• Many organizations prefer to use analogy methods
– Yesterday’s weather: same as today’s 70% of the time
• Use same size, productivity, cost, schedule as last project
– Too many parameters to estimate in parametric models
• However, next-day’s weather may not be the same
– Or next-project’s cost driver settings
• Want to adjust analogy estimate to reflect differences
– This is what Agile COCOMO II does
© USC-CSSE
3
University of Southern California
Center for Systems and Software Engineering
Nature of Agile COCOMO II
• Offers choice of analogy baseline
– Size Quantity: Equivalent KSLOC, Function Points,
User Stories or Use Cases
– Resources Needed: Dollars, Person-Months, Ideal
Person-Weeks
– Productivity: Dollars per Size Quantity, Size Quantity
per Person-Month or Ideal Person-Week
• Modifies analogy baseline to reflect newproject deltas
© USC-CSSE
4
University of Southern California
Center for Systems and Software Engineering
Outline
• Motivation
• Nature of Agile COCOMO II
• Extensions to Case-Based Reasoning
• Example of Use: WellPoint
• Further Potential Extensions
© USC-CSSE
5
University of Southern California
Center for Systems and Software Engineering
Extensions to Case-Based Reasoning
• Searches project metadata for project closest to
project being estimated (e.g., WellPoint 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)
© USC-CSSE
6
University of Southern California
Center for Systems and Software Engineering
WellPoint Systems Impacted
WS1
WS2
WS3
WS4
WS5
WP1
✔
✔
✔
✔
✔
WP2
WP3
✔
✔
…
WP5
…
WP9
…
WP31
WP32
✔
✔
✔
✔
© USC-CSSE
✔
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University of Southern California
Center for Systems and Software Engineering
Regression – Impacted Systems 1/3
© USC-CSSE
8
University of Southern California
Center for Systems and Software Engineering
Regression – Impacted Systems 2/3
Y  mX  b
Variabl
e
b
m
Coefficient
Standard Error
-14,543.0990 13,190.0154
3,238.0697
671.3541

Total Hours  3,238.0697#Req. 14,543.099
Average Prediction %Error = 231.61%
© USC-CSSE
9
University of Southern California
Center for Systems and Software Engineering
Regression – Impacted Systems 3/3
Projec
t
Effort PH Predictio
n
%Erro
r
WP15
2,847.5
27551.81
867.58
WP18
8,135.25
46980.22
477.49
WP1
4,348
76122.85
1,650.8
WP19
16,393.75 -8066.96
149.21
WP2
52,474
124693.9
137.63
WP20
49,290.5
90.09
WP3
17,433
37266.02
113.77
WP22
22,577.25 -11305.03
150.07
WP4
8,212.5
11361.46
38.34
WP23
35,318.25 46980.22
33.02
WP5
27,922
43742.15
56.66
WP25
17,550.25 -8066.96
145.96
WP8
4,012.75
14599.53
263.83
WP26
7,041.75
-4828.89
168.58
WP9
326,864
173264.9
46.99
WP27
6,535
-1590.82
124.34
WP10
18,405.4
27551.81
49.69
WP29
55,342
43742.15
20.96
WP11
22,464.8
-1590.82
107.08
WP30
15,510.25 50218.29
223.77
WP12
3,338.75
-1590.82
147.65
WP31
26,874.25 4885.319
81.82
WP13
2,104.75
-4828.89
329.43
WP32
23,072.25 85837.06
272.04
WP14
8,631
4885.319
43.40
© USC-CSSE
4885.319
10
University of Southern California
Center for Systems and Software Engineering
Regression – Requirements Impacting Systems 1/3
© USC-CSSE
11
University of Southern California
Center for Systems and Software Engineering
Regression – Requirements Impacting Systems 2/3
Y  mX  b
Variabl
e
b
m
Coefficient
Standard Error
-14,596.4122 9,681.6517
690.5084
96.8136

Total Hours  690.5084#Req. 14,569.4122
Average Prediction %Error = 205.41%
© USC-CSSE
12
University of Southern California
Center for Systems and Software Engineering
Regression – Requirements Impacting Systems 3/3
Projec
t
Effort PH Predictio
n
%Erro
r
WP15
2,847.5
-2,140.261 175.16
WP18
8,135.25
84,173.29
WP1
4,348
26,861.09
517.8
WP19
16,393.75 -9,045.345 155.18
WP2
52,474
99,364.48
89.36
WP20
49,290.5
WP3
17,433
2,693.298
84.55
WP22
22,577.25 -4,902.294 121.71
WP4
8,212.5
-5,592.803 168.1
WP23
35,318.25 16,503.47
53.27
WP5
27,922
26,861.09
3.8
WP25
17,550.25 18,574.99
5.84
WP8
4,012.75
1,312.282
67.3
WP26
7,041.75
-7,664.328 208.84
WP9
326,864
232,632.6
28.83
WP27
6,535
30,313.64
363.87
WP10
18,405.4
44,123.8
139.73
WP29
55,342
62,767.53
13.42
WP11
22,464.8
1,312.282
94.16
WP30
15,510.25 64,839.06
318.04
WP12
3,338.75
-11,807.38 453.65
WP31
26,874.25 19,265.5
28.31
WP13
2,104.75
-8,354.836 497.95
WP32
23,072.25 70,363.12
204.97
WP14
8,631
34,456.69
299.22
© USC-CSSE
934.67
-4,211.786 108.54
13
University of Southern California
Center for Systems and Software Engineering
Agile COCOMO II
• Average Prediction %Error = 160.93%
• 30.52% improvement from Systems
Impacted Linear regression
• 21.65% improvement from Requirements
Impacting Systems Linear regression
© USC-CSSE
14
University of Southern California
Center for Systems and Software Engineering
Agile COCOMO II
Projec
t
Effort PH Predictio
n
%Erro
r
WP15
2,847.5
3,412.96
19.86
WP18
8,135.25
24,169.32
197.09
WP1
4,348
38,306.02
781.00
WP19
16,393.75 19,641.99
19.81
WP2
52,474
26,519.54
49.46
WP20
49,290.5
16,764.93
25.74
WP3
17,433
3,412.96
80.42
WP22
22,577.25 13,890.41
60.67
WP4
8,212.5
3,621.05
55.91
WP23
35,318.25 30,253.18
72.38
WP5
27,922
3,621.05
9.76
WP25
17,550.25 19,641.99
178.94
WP8
4,012.75
91,304.76
72.07
WP26
7,041.75
39,283.98
501.13
WP9
326,864
18,613.15
1.13
WP27
6,535
18,613.15
66.37
WP10
18,405.4
26,415.09
17.58
WP29
55,342
18,613.15
20.01
WP11
22,464.8
16,481.21
393.63
WP30
15,510.25 5,803.60
78.40
WP12
3,338.75
19,641.99
833.22
WP31
26,874.25 45,652.38
97.87
WP13
2,104.75
14,570.71
68.82
WP32
23,072.25 3,412.96
19.86
WP14
8,631
38,306.02
781.00
© USC-CSSE
15
University of Southern California
Center for Systems and Software Engineering
Outline
• Motivation
• Nature of Agile COCOMO II
• Extensions to Case-Based Reasoning
• Example of Use: WellPoint
• Further Potential Extensions
© USC-CSSE
16