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| Criterion | ![]() Engineering Goal Question Metric | ![]() UX Research HEART Framework | ![]() 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. | 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 | High |
Timedifferent | 90-180 min | 120 min initial, dann laufend | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-6 | 3-6 | 1-8 |
Formatdifferent | Workshop | Workshop + async | Workshop + async |
Outputdifferent | GQM Table, Metric Profiles | HEART-GSM Table, Dashboard | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | MetricsMeasurementEngineeringAlignment | MetricsUX researchMeasurementSatisfaction | ForecastingFlowDelivery |
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Methods with strong topical overlap with the current selection, not yet in the comparison.






