Expert COSYSMO

1. Expert COSYSMO Ray Madachy, Ricardo Valerdi USC Center for Systems and Software Engineering MIT Lean Aerospace Initiative madachy@usc.edu, rvalerdi@mit.edu 22nd International Forum on COCOMO and Systems/Software Cost ModelingNovember 1, 2007 ©USC-CSSE

2. Introduction • An expert system tool for systems engineering risk assessment based on the Constructive Systems Engineering Cost Model (COSYSMO) [Valerdi 2005] • Automatically identifies project risks in conjunction with cost estimation similar to Expert COCOMO [Madachy 1997] • Supports project planning by identifying, categorizing, quantifying, and prioritizing system-level risks • Includes 98 risk conditions • Risk situations are characterized by combinations of cost driver values indicating increased effort with a potential for more problems • Simultaneously calculates cost to enable tradeoffs with risk ©USC-CSSE

3. Method • Analyzes patterns of cost driver ratings submitted for a COSYSMO cost estimate against pre-determined risk rules • Identifies individual risks that an experienced systems engineering manager might recognize but often fails to take into account • Helps users determine and rank sources of project risk. With these risks, mitigation plans can be created based on the relative risk severities and provided advice ©USC-CSSE

4. Method (cont.) • COSYSMO cost factor combinations used as abstractions for formulating risk heuristics • E.g. if Architecture Understanding = Very Low and Level of Service Requirements = Very High, then there is a high risk • Since systems with high service requirements are more difficult to implement especially when the architecture is not well understood • Elicitation of knowledge from systems engineering domain experts in CSSE-sponsored workshops • Survey used to identify and quantify risks • Devised knowledge representation scheme and risk quantification algorithm ©USC-CSSE

5. Risk Conditions ©USC-CSSE

6. # categories # category risks å å = Project Risk risk level * effort mu ltiplier p roduct i , j i , j = = j 1 i 1 Project Risk Product risk Process risk Personnel risk Platform risk Risk Taxonomy and Weighting where risk level = 1 moderate 2 high 4 very high effort multiplier product= (driver #1 effort multiplier) * (driver #2 effort multiplier) ... * (driver #n effort multiplier). ©USC-CSSE

7. Next: Finer Assignment of Risk Levels ATTRIBUTE 1 very low extra high very low very high high increasing risk moderate ATTRIBUTE2 very high discretized into ATTRIBUTE 1 VERY LOW LOW NOMINAL HIGH VERY HIGH EXTRA HIGH VERY LOW MODERATE HIGH VERY HIGH LOW MODERATE HIGH ATTRIBUTE 2 NOMINAL MODERATE HIGH VERY HIGH ©USC-CSSE

8. Expert COSYSMO Inputs ©USC-CSSE

9. Expert COSYSMO Outputs ©USC-CSSE

10. Current and Future Work • Currently scaling the risk summary outputs for each category and defining ranges for low, medium and high risks • Create more granular risk quantification rules • Generate expert risk mitigation advice for each risk condition, and provide that automated guidance to users to help develop their own mitigation actions • Add rules to detect COSYSMO input anomalies • Systems engineering risk data from industrial projects will be analyzed to enhance and refine the technique • Perform statistical testing • Domain experts from industry and government will continue to provide feedback and clarification • Supporting surveys and workshops will be continued • Exploration of alternate risk and uncertainty approaches to integrate multiple risk management viewpoints into a more complete risk management framework ©USC-CSSE

11. References • R. Madachy, Heuristic Risk Assessment Using Cost Factors, IEEE Software, May 1997 • Valerdi R., The Constructive Systems Engineering Cost Model (COSYSMO), PhD Dissertation, University of Southern California, Los Angeles, CA, May 2005 • http://csse.usc.edu/tools/expert_cosysmo.php ©USC-CSSE