integrating case-based, analogy-based, and parameter-based estimation via agile cocomo ii

University of Southern CaliforniaCenter for Systems and Software Engineering

1© USC-CSSE

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


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Outline

• Motivation

• Nature of Agile COCOMO II

• Extensions to Case-Based Reasoning

• Example of Use: WellPoint

• Further Potential Extensions


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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


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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 new-project deltas


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Outline

• Motivation






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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)


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WellPoint Systems ImpactedWS1 WS2 WS3 WS4 WS5

WP1 ✔ ✔ ✔ ✔ ✔

WP2 ✔WP3 ✔…

WP5 ✔…

WP9 ✔…

WP31 ✔WP32 ✔ ✔


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Regression – Impacted Systems 1/3


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€

Total Hours = 3,238.0697×# Req.−14,543.099€

Y = mX +bVariable

Coefficient Standard Error

b -14,543.0990 13,190.0154

m 3,238.0697 671.3541

Average Prediction %Error = 231.61%


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Project Effort PH Prediction %Error

WP1 4,348 76122.85 1,650.8

WP2 52,474 124693.9 137.63

WP3 17,433 37266.02 113.77

WP4 8,212.5 11361.46 38.34

WP5 27,922 43742.15 56.66

WP8 4,012.75 14599.53 263.83

WP9 326,864 173264.9 46.99

WP10 18,405.4 27551.81 49.69

WP11 22,464.8 -1590.82 107.08

WP12 3,338.75 -1590.82 147.65

WP13 2,104.75 -4828.89 329.43

WP14 8,631 4885.319 43.40

WP15 2,847.5 27551.81 867.58

WP18 8,135.25 46980.22 477.49

WP19 16,393.75 -8066.96 149.21

WP20 49,290.5 4885.319 90.09

WP22 22,577.25 -11305.03 150.07

WP23 35,318.25 46980.22 33.02

WP25 17,550.25 -8066.96 145.96

WP26 7,041.75 -4828.89 168.58

WP27 6,535 -1590.82 124.34

WP29 55,342 43742.15 20.96

WP30 15,510.25 50218.29 223.77

WP31 26,874.25 4885.319 81.82

WP32 23,072.25 85837.06 272.04


12© USC-CSSE


€

Total Hours = 690.5084×#Req.−14,569.4122€

Y = mX +bVariable

Coefficient Standard Error

b -14,596.4122 9,681.6517

m 690.5084 96.8136

Average Prediction %Error = 205.41%


13© USC-CSSE


Project Effort PH Prediction %Error

WP1 4,348 26,861.09 517.8

WP2 52,474 99,364.48 89.36

WP3 17,433 2,693.298 84.55

WP4 8,212.5 -5,592.803 168.1

WP5 27,922 26,861.09 3.8

WP8 4,012.75 1,312.282 67.3

WP9 326,864 232,632.6 28.83

WP10 18,405.4 44,123.8 139.73

WP11 22,464.8 1,312.282 94.16

WP12 3,338.75 -11,807.38 453.65

WP13 2,104.75 -8,354.836 497.95

WP14 8,631 34,456.69 299.22

WP15 2,847.5 -2,140.261 175.16

WP18 8,135.25 84,173.29 934.67

WP19 16,393.75 -9,045.345 155.18

WP20 49,290.5 -4,211.786 108.54

WP22 22,577.25 -4,902.294 121.71

WP23 35,318.25 16,503.47 53.27

WP25 17,550.25 18,574.99 5.84

WP26 7,041.75 -7,664.328 208.84

WP27 6,535 30,313.64 363.87

WP29 55,342 62,767.53 13.42

WP30 15,510.25 64,839.06 318.04

WP31 26,874.25 19,265.5 28.31

WP32 23,072.25 70,363.12 204.97


14© USC-CSSE

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


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Agile COCOMO IIProject Effort PH Prediction %Error

WP1 4,348 38,306.02 781.00

WP2 52,474 26,519.54 49.46

WP3 17,433 3,412.96 80.42

WP4 8,212.5 3,621.05 55.91

WP5 27,922 3,621.05 9.76

WP8 4,012.75 91,304.76 72.07

WP9 326,864 18,613.15 1.13

WP10 18,405.4 26,415.09 17.58

WP11 22,464.8 16,481.21 393.63

WP12 3,338.75 19,641.99 833.22

WP13 2,104.75 14,570.71 68.82

WP14 8,631 38,306.02 781.00

WP15 2,847.5 3,412.96 19.86

WP18 8,135.25 24,169.32 197.09

WP19 16,393.75 19,641.99 19.81

WP20 49,290.5 16,764.93 25.74

WP22 22,577.25 13,890.41 60.67

WP23 35,318.25 30,253.18 72.38

WP25 17,550.25 19,641.99 178.94

WP26 7,041.75 39,283.98 501.13

WP27 6,535 18,613.15 66.37

WP29 55,342 18,613.15 20.01

WP30 15,510.25 5,803.60 78.40

WP31 26,874.25 45,652.38 97.87

WP32 23,072.25 3,412.96 19.86

integrating case-based, analogy-based, and parameter-based estimation via agile cocomo ii

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