University of Southern California Center 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

University of Southern California Center for Systems and Software Engineering 2 © USC-CSSE Outline Motivation Nature of Agile COCOMO II Extensions to Case-Based Reasoning Example of Use: WellPoint Further Potential Extensions

University of Southern California Center for Systems and Software Engineering 3 © USC-CSSE 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

University of Southern California Center for Systems and Software Engineering 4 © USC-CSSE 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

University of Southern California Center for Systems and Software Engineering 5 © USC-CSSE Outline Motivation Nature of Agile COCOMO II Extensions to Case-Based Reasoning Example of Use: WellPoint Further Potential Extensions

University of Southern California Center for Systems and Software Engineering 6 © USC-CSSE 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 ( $5M)

University of Southern California Center for Systems and Software Engineering 7 © USC-CSSE WellPoint Systems Impacted WS1WS2WS3WS4WS5 WP1 ✔✔✔✔✔ WP2 ✔ WP3 ✔ … WP5 ✔ … WP9 ✔ … WP31 ✔ WP32 ✔✔

University of Southern California Center for Systems and Software Engineering 8 © USC-CSSE Regression – Impacted Systems 1/3

University of Southern California Center for Systems and Software Engineering 9 © USC-CSSE Regression – Impacted Systems 2/3 Variabl e CoefficientStandard Error b-14, , m3, Average Prediction %Error = %

University of Southern California Center for Systems and Software Engineering 10 © USC-CSSE Regression – Impacted Systems 3/3 Projec t Effort PHPredictio n %Erro r WP14, ,650.8 WP252, WP317, WP48, WP527, WP84, WP9326, WP1018, WP1122, WP123, WP132, WP148, WP15 2, WP18 8, WP19 16, WP20 49, WP22 22, WP23 35, WP25 17, WP26 7, WP27 6, WP29 55, WP30 15, WP31 26, WP32 23,

University of Southern California Center for Systems and Software Engineering 11 © USC-CSSE Regression – Requirements Impacting Systems 1/3

University of Southern California Center for Systems and Software Engineering 12 © USC-CSSE Regression – Requirements Impacting Systems 2/3 Variabl e CoefficientStandard Error b-14, , m Average Prediction %Error = %

University of Southern California Center for Systems and Software Engineering 13 © USC-CSSE Regression – Requirements Impacting Systems 3/3 Projec t Effort PHPredictio n %Erro r WP14,34826, WP252,47499, WP317,4332, WP48, , WP527,92226, WP84, , WP9326,864232, WP1018, , WP1122,464.81, WP123, , WP132, , WP148,63134, WP15 2, , WP18 8, , WP19 16, , WP20 49, , WP22 22, , WP23 35, , WP25 17, , WP26 7, , WP27 6,535 30, WP29 55,342 62, WP30 15, , WP31 26, , WP32 23, ,

University of Southern California Center for Systems and Software Engineering 14 © USC-CSSE Agile COCOMO II Average Prediction %Error = % 30.52% improvement from Systems Impacted Linear regression 21.65% improvement from Requirements Impacting Systems Linear regression

University of Southern California Center for Systems and Software Engineering 15 © USC-CSSE Agile COCOMO II Projec t Effort PHPredictio n %Erro r WP14,348 38, WP252,474 26, WP317,433 3, WP48, , WP527,922 3, WP84, , WP9326,864 18, WP1018, , WP1122, , WP123, , WP132, , WP148,631 38, WP15 2, , WP18 8, , WP19 16, , WP20 49, , WP22 22, , WP23 35, , WP25 17, , WP26 7, , WP27 6,535 18, WP29 55,342 18, WP30 15, , WP31 26, , WP32 23, ,

University of Southern California Center for Systems and Software Engineering 16 © USC-CSSE Outline Motivation Nature of Agile COCOMO II Extensions to Case-Based Reasoning Example of Use: WellPoint Further Potential Extensions