Previous Lecture DDP: quantitative risk management for Req. Engineering Defect Detection Prevention CS/SE 3RA3 Ryszard Janicki Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada Ryszard Janicki Defect Detection Prevention 1/8 Previous Lecture DDP: quantitative risk management for Req. Engineering Previous Lecture: Risk Analysis Risk identification what system-specific risks? Risk control Risk assessment likely? severe, likely consequences? Ryszard Janicki countermeasures t as new reqs Defect Detection Prevention 2/8 Previous Lecture DDP: quantitative risk management for Req. Engineering Impact matrix Effectiveness matrix Optimal Balance DDP = Defect Detection Prevention Technique and tool developed at NASA in 2003 Quantitative support for Identify-Assess-Control cycles Three steps: Elaborate I t risk i k Impact matrix Elaborate countermeasure Effectiveness matrix Ryszard Janicki Determine optimal balance risk reduction / countermeasure cost Defect Detection Prevention 3/8 Previous Lecture DDP: quantitative risk management for Req. Engineering Impact matrix Effectiveness matrix Optimal Balance Three Steps 1 Build a risk-consequence table (impact matrix) with domain experts for: prioritizing risks by critical impact on all objectives highlighting the most risk-driving objectives 2 Build a risk-countermeasure table with domain experts for: estimating risk reduction by alternative countermeasures highlighting most globally effective countermeasures 3 Determine optimal balance: risk reduction vs. countermeasure cost Ryszard Janicki Defect Detection Prevention 4/8 Impact matrix: example for library system For each objective obj and risk r , we provide: Impact(r , obj) = estimated loss of satisfaction of obj by r 0 (no loss) → 1 (total loss) Risk Criticality: P RC (r ) = Likelihood(r ) obj Impact(r , obj)Weight(obj) Objective Loss: P Loss(obj) = Weight(obj) r Impact(r , obj)Likelihood(r ) Effectiveness matrix: example for library system For each countermeasure cm, weighted risk r , we provide: Reduction(cm, r ) = estimated reduction of r if cm applied 0 (no reduction) → 1 (risk eliminated) Combines Reduction: Q CR(r ) = 1 − cm (1 − Reduction(cm, r )) P Overall Effect: OE (cm) = r Reduction(cm, r )Criticality (r ) Previous Lecture DDP: quantitative risk management for Req. Engineering Impact matrix Effectiveness matrix Optimal Balance Determine optimal balance: risk reduction vs. countermeasure cost Cost of each countermeasure cm to be estimated with domain experts DDP can then visualize: risk balance charts: residual impact of each risk on all objectives if cm is selected optimal combinations of countermeasures for risk balance under cost constraints simulated annealing search for near-optimal solutions optimality criterion can be set by user e.g. “maximize satisfaction of objectives under this cost threshold” “minimize cost above this satisfaction threshold” Ryszard Janicki Defect Detection Prevention 7/8 Previous Lecture DDP: quantitative risk management for Req. Engineering Impact matrix Effectiveness matrix Optimal Balance IMPORTANT NEVER TREAT LITERARY NUMBERS OBTAINED BY ANY OF THE METHODS DISCUSSED! NEVER TREAT THE EQUATIONS AS LAWS OF NATURE! ALWAYS TREAT THE OUTCOME AS A DIRECTION, NOT AN EXACT RESULT! Ryszard Janicki Defect Detection Prevention 8/8
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