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Selected:SAAM (Software Architecture Analysis Method)Hypothesis Prioritization CanvasMVP Test MatrixLearning Card
| Criterion |  Architecture SAAM (Software Architecture Analysis Method) |  Product Discovery Hypothesis Prioritization Canvas |  Product Discovery MVP Test Matrix |  Product Discovery Learning Card |
|---|---|---|---|---|
Purposedifferent | SAAM helps teams judge an architecture’s suitability for selected tasks and changes. It is particularly useful for examining modifiability and comparing architecture alternatives with clear reasoning. | When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | When several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. | The Learning Card connects traceable test results to a reasoned next action while keeping the limits of the insight visible. |
Complexitydifferent | High | Medium | Medium | Low |
Timedifferent | Preparation, scenario analysis, and findings review; effort depends on scope | 60-90 min | 45-75 min | 25-40 min |
Participantsdifferent | Architecture experts and stakeholders for scenarios and weighting | 3-8 | 2-8 | 1-5 |
Formatdifferent | Workshop + async | Workshop | Workshop | Workshop + async |
Outputdifferent | Domain functions mapped to components and connections, Quality attributes and evaluation criteria, Typical tasks and their effects on the architecture, Reasoned evaluation or comparison of alternatives | Prioritization Canvas, Hypothesis Backlog | Test Matrix, Test Plan | Learning Card with evidence and next action |
Tagsno overlap | ArchitectureChangeAnalysisComparisonQuality | ExperimentsPrioritizationDiscoveryHypothesis | ExperimentsValidationDiscoveryOptions | ExperimentsValidationDiscoveryLearning |