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University of Southern California Center for Systems and Software Engineering Risk Calculation Case Studies CS 510 Software Engineering Supannika Koolmanojwong.

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Presentation on theme: "University of Southern California Center for Systems and Software Engineering Risk Calculation Case Studies CS 510 Software Engineering Supannika Koolmanojwong."— Presentation transcript:

1 University of Southern California Center for Systems and Software Engineering Risk Calculation Case Studies CS 510 Software Engineering Supannika Koolmanojwong

2 University of Southern California Center for Systems and Software Engineering Outline Risk Management Case Studies Risk Reduction Leverage Case Studies 2

3 University of Southern California Center for Systems and Software Engineering Importance of Risk Management Avoiding Disasters –80-20 rule Avoiding Rework –Rework of erroneous requirements, design, code typically consumes 40-50% of the total costs of software development –Prime opportunity to improve productivity Avoiding Overkill –Focus effort on the critical areas that make a difference Stimulating Win-Win Situations –“Best and final offer” negotiation on software contracts leads to cheapest wins 3

4 University of Southern California Center for Systems and Software Engineering Software project’s general risks Error prone products Costly, late fixes Out-of-control projects Out-of-control products 4 testing and verification Early requirements and design v&v Project planning and control functions Config mgnt and QA functions

5 University of Southern California Center for Systems and Software Engineering Requirements Risks Developing the wrong software functions Developing the wrong UI Gold Plating Continuing stream of requirements changes 5

6 University of Southern California Center for Systems and Software Engineering Straining Computer Science Capabilities (1989) Distributed Processing Artificial Intelligence Domains Human-machine performance Algorithm speed and accuracy Information privacy and security protection High reliability and fault tolerance 6

7 University of Southern California Center for Systems and Software Engineering Decision-Driver If decisions had been driven by factors other than technical and management achievability, frequently be the source of critical software-risk item. 7 Politically driven decisions Choice of equipment, subcontractor, schedule & budget, allocation of responsibilities Marketing-driven decisions Gold plating, choice of eq, schedule & budget

8 University of Southern California Center for Systems and Software Engineering Decision – Driver (cont) Solution-driven vs problem-driven decisions In-house components and tools, product champion Short-term vs Long-term decisions Staffing – available basis rather than the qualified ones Software reuse – often flaky and incompatible Premature review – fixed review date 8

9 University of Southern California Center for Systems and Software Engineering Pareto phenomena 80% of the contribution comes from 20% of the contributors –20% of the modules contribute 80% of the cost –20% of the modules contribute 80% of the errors –20% of the errors cause 80% of the down time –20% of the errors consume 80% of the cost to fix –20% of the modules consume 80% of the execution time 9

10 University of Southern California Center for Systems and Software Engineering Uncertainty Areas and unresolved questions Mission requirements –Vague top-level mission statements but no clear definition Life-cycle concept of option –Boundaries between maintenance and incremental development or preplanned product improvement System performance drivers –Where to measure (CPU, main memory, communication) UI characteristics –IKIWISI Interfacing system characteristics –Sharing common resources? Common modules? 10

11 University of Southern California Center for Systems and Software Engineering Dealing with compound risks Pushing technology on more than one front –Too many new technology at the same time Pushing technology with key staff shortages Meeting vague user requirements on an ambitious schedule Untried hardware with an ambitious schedule Unstable interfaces with an untried subcontractor 11

12 University of Southern California Center for Systems and Software Engineering Make or Buy Decision Analysis 12 Geographic DBMS $1,200K $1,800K $700K $1,500K $2,500K $1,000K $2,000K BUILD REUSE BUY EASY HARD SMALL MOD LARGE MOD EASY HARD SMALL MOD LARGE MOD = (0.4*1200K) + (0.6*1800K) = $1,560K =$1,450K =$1,400K

13 University of Southern California Center for Systems and Software Engineering Do IV&V or not? 13 P(Loss) = 0.36, Find CE NO IV&V DO IV&V 0.04 Don’t Find CE 0.6 NO CE 0.3 Find CE 0.1 Don’t Find CE 0.6 NO CE Size(Loss) = $0.5 M $20.5M $0.5M Size(Loss) = $0 M $20M $0M $0.18M $0.82 M $0.30 M $1.3M $0 $20M $0 $2M

14 University of Southern California Center for Systems and Software Engineering 10/14/05©USC-CSE14 Risk Reduction Leverage (RRL) RRL - RE BEFORE - RE AFTER RISK REDUCTION COST · Spacecraft Example LOSS (UO) PROB (UO) RE B B LONG DURATION TEST $20M 0.2 $4M FAILURE MODE TESTS $20M 0.2 $4M PROB (UO) RE A A 0.05 $1M 0.07 $1.4M COST$2M$0.26M RRL = = 10


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