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| Criterion |  Decision Making Radar |  Architecture LASR (Lightweight Approach for Software Reviews) |  Product Discovery Experiment Canvas |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | When capabilities, maturity levels, or market positions need to be compared across several dimensions, overview is easily lost. Radar brings different characteristics into a shared picture and shows patterns faster than tables do. | LASR helps development teams identify where their software falls short of important quality goals and which improvements to consider. A small group with existing system knowledge can make an initial assessment and investigate open questions in more detail. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. |
Complexitydifferent | Medium | Medium | Low | Medium |
Timedifferent | 1-2 h | 3–4 h for initial assessment; preparation and deeper analysis additional | 30-60 min | 1-5 Tage |
Participantsdifferent | 2-10 | 1 or a small team; particularly suitable for 3–4 people | 1-5 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Radar Map, Capability Scores, Action Areas | Lean Mission Statement describing the software’s purpose, Prioritized quality goals and evaluation criteria, Risks and estimated gaps against quality goals, More closely examined findings and open checks | Completed Experiment Canvas, Success Metric | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | AssessmentPortfolioCapability | ArchitectureQualityRiskAssessmentLean | ExperimentsValidationDiscoveryHypothesis | ValidationExperimentsDemandDiscovery |