A/B Testing Workflow Class Diagram Template

This class diagram template visualizes the full A/B testing workflow as a structured system of interconnected classes and relationships. Each phase—Hypothesis, Experiment Design, Variant Shipment, and Decision—is represented as a distinct class with its own attributes and methods, showing how data flows between stages. The diagram captures key objects such as Hypothesis (with fields like metric, baseline, and expected lift), ExperimentDesign (sample size, traffic split, duration), Variant (control, treatment, feature flags), and DecisionEngine (statistical significance, p-value, rollout action). Relationships between classes clarify dependencies, inheritance, and associations, giving teams a precise blueprint of how their testing infrastructure is organized.

## When to Use This Template

Use this class diagram when you are architecting or documenting an A/B testing platform, onboarding engineers to an existing experimentation system, or aligning cross-functional teams on how testing components interact. It is especially valuable during the design phase of a new feature experiment, when product managers need to communicate requirements to developers, or when data scientists want to formalize the statistical decision logic within a shared codebase. Teams scaling their experimentation programs will also find it useful for identifying gaps in their current class structure before adding new experiment types.

## Common Mistakes to Avoid

One frequent mistake is collapsing all experiment logic into a single monolithic class, which obscures the separation of concerns between hypothesis formulation, variant delivery, and result analysis—keep these as distinct classes with clear interfaces. Another error is omitting the relationship between the DecisionEngine and the Hypothesis class; without this link, the diagram fails to show how outcomes are evaluated against original assumptions. Avoid over-engineering the diagram with too many subclasses early on; start with core classes and add inheritance only when real variants of behavior exist in your system. Finally, do not neglect to document multiplicity on associations—knowing whether one Hypothesis can spawn multiple ExperimentDesigns, or whether a Variant belongs to exactly one Experiment, is critical for developers implementing the system correctly.