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| Criterion |  Engineering Goal Question Metric |  UX Research HEART Framework |  Agile NoEstimates |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | Helps clarify technical problems, hypotheses, and next steps in concrete terms. It breaks a technical problem into testable parts. The result is captured as a GQM table and metric briefs. | Helps clarify observations, needs, and patterns in concrete terms. It groups observations into patterns, questions, and decisions. The result is captured as a HEART-GSM table and dashboard. | When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. | Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as Forecast Percentiles, Throughput Dataset, and Risk Communication. |
Complexitydifferent | Medium | Medium | Medium | High |
Timedifferent | 90-180 min | 120 min initial, dann laufend | laufend | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-6 | 3-6 | 2-12 | 1-8 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | GQM Table, Metric Profiles | HEART-GSM Table, Dashboard | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | MetricsMeasurementEngineeringAlignment | MetricsUX researchMeasurementSatisfaction | EstimationForecastingFlow | ForecastingFlowDelivery |