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Criterion
Quality Attribute Workshop workspace showing the question, observations, and next decision.
Architecture
Quality Attribute Workshop
Design sheets and interface cards connect to jointly written pseudocode with highlighted unresolved questions.
Architecture
ARID (Active Reviews for Intermediate Designs)
Paper illustration of MVP Test Matrix with a method-specific labelled workspace.
Product Discovery
MVP Test Matrix
Paper illustration of a Learning Card with four fields for hypothesis, observation, insight, and action.
Product Discovery
Learning Card
Purposedifferent
A Quality Attribute Workshop translates quality requirements into concrete scenarios and architecture impulses. The method brings product, engineering, and operations to the same view of desired properties.ARID examines how well developers can work with a technical design. It focuses on a part of the system that can still be revised, between the architecture overview and the detailed specification.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
MediumMediumMediumLow
Timedifferent
2-4 h2–4 h review as a planning range; preparation and evaluation additional45-75 min25-40 min
Participantsdifferent
5-15Designers, developers as users, and facilitation2-81-5
Formatdifferent
WorkshopWorkshop + asyncWorkshopWorkshop + async
Outputdifferent
Quality Scenarios, Priority List, Architecture ConcernsPrioritized development tasks, Jointly produced code or pseudocode, Observed difficulties and open design questions, Proposed design revisionsTest Matrix, Test PlanLearning Card with evidence and next action
Tagsno overlap
RequirementsArchitectureWorkshop
ArchitectureEngineeringFeedbackValidationTasks
ExperimentsValidationDiscoveryOptions
ExperimentsValidationDiscoveryLearning
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