Figma's component variant system is expressive. You can define a button with two sizes (small, large), three styles (primary, secondary, ghost), four states (default, hover, focus, disabled), and a boolean for whether it has a leading icon. That is 2 x 3 x 4 x 2 = 48 variant nodes in Figma. The design file is explicit about every combination that exists.
The question is what the equivalent React component looks like. Most code generation tools produce one of two outputs that are both wrong: they flatten all 48 variants into 48 separate components, or they collapse everything into a single component with a giant variant string prop that accepts a free-form value like "primary-large-default-with-icon". Neither approach is how a senior engineer would write the component.
The right output is a component with typed, composable props: size, style, state, leadingIcon. Each prop is independently selectable, and the component handles the visual combinations internally. Getting code generation to produce this output requires understanding how to structure variants in Figma before the generation step.
How Figma Variants Map to Props
Figma component variants are defined by property dimensions. Each property has a name and a set of possible values. A button with Size: [Small, Large], Style: [Primary, Secondary, Ghost], and State: [Default, Hover, Focus, Disabled] has three property dimensions.
In React, each property dimension becomes a prop. The prop name is the property name, lowercased and camelCased if needed. The prop type is a union of the possible values, expressed as a string literal union:
type ButtonProps = {
size: 'small' | 'large';
style: 'primary' | 'secondary' | 'ghost';
state: 'default' | 'hover' | 'focus' | 'disabled';
leadingIcon?: boolean;
};The mapping is clean when the Figma property names and values follow predictable conventions. It breaks down when they do not. "Size: [S, M, L, XL]" maps cleanly. "Size: [button-small, button-default, button-large, button-extra]" produces prop values that need manual cleanup before they are usable in code.
This is the first design-side discipline that affects generation quality: property names and values in Figma should use the same vocabulary your codebase uses. If your engineers use size="sm" and size="lg", the Figma property values should be sm and lg, not small and large.
Interactive States Are a Special Case
The State property dimension deserves separate treatment because interactive states in code are not exactly props in the same sense that size and style are.
Hover is a CSS pseudo-class, not a React prop. A consumer of your button component does not pass state="hover". The hover state appears automatically when the cursor enters the component. A code generator that produces a state prop with a hover value is generating code that is correct for design documentation but incorrect for production use.
Focus is similar. It is browser-managed when the user navigates with the keyboard. A code generator should produce the CSS :focus-visible styles for the focus state, not a prop.
Disabled is genuinely a prop. isDisabled: boolean is valid React. When isDisabled is true, the component should apply the disabled visual styles and set disabled on the underlying HTML element.
Loading is also a genuine prop: isLoading: boolean. When true, the component shows a spinner and typically disables interaction.
A generator that understands this distinction will emit CSS for hover and focus states, and props for disabled and loading states. A generator that flattens the entire State dimension into a prop generates code that looks reasonable but cannot actually be used.
Boolean Properties and When to Use Them
Figma boolean properties are useful for features that are either present or absent: a leading icon, a trailing icon, a badge count, a clear button. These map directly to optional React props or optional children.
The question for code generation is whether a boolean property should become a required boolean prop (hasLeadingIcon: boolean), an optional boolean prop (leadingIcon?: boolean), or a slot prop (leadingIcon?: ReactNode).
The answer depends on what the designer intended. If the property toggles visibility of a fixed icon, a boolean prop is correct. If the property controls whether an arbitrary icon can appear (and different consumers might want different icons), a slot prop is correct. The generator needs this intent expressed in the Figma file through naming conventions.
A property named Leading icon: [True, False] tells the generator to produce a boolean prop. A property named Leading icon slot: [With icon, Without] or a component with a named slot frame signals that the consumer should pass content.
Variant Naming Conventions That Make Generation Reliable
The variant naming decisions that have the largest impact on generation quality:
Use the same case as your codebase. If your TypeScript uses camelCase props, use camelCase in Figma property names. If you use kebab-case CSS classes, those can stay separate from the prop names, but the Figma vocabulary should match the API vocabulary.
Avoid space-separated multi-word property names. "Leading icon" as a Figma property name becomes leadingIcon as a prop, which is the right output, but only if the generator knows to apply that transformation. Make the conversion explicit by using camelCase directly: leadingIcon in Figma produces leadingIcon in code with no ambiguity.
Separate visual style from state. "Primary hover" as a variant value conflates a visual style (primary) with a state (hover). Keep these as separate dimensions: Style: Primary and State: Hover. The generator can then produce independent props and the right CSS pseudo-class handling.
Where Code Generation Still Requires Human Judgment
We are direct about this: there is a class of variant-to-props decisions that a generator cannot make without a type system hint from the design side. Complex compound variants, conditional rendering based on multiple prop combinations, and accessibility attribute management for stateful components all require engineering decisions that the Figma file alone cannot express.
For those cases, DesignVerse generates a typed component scaffold with the props correctly declared and the visual variants implemented, but with comment stubs for the behavior logic that requires human authoring. A generator that silently omits these stubs and produces code that compiles but behaves incorrectly is more dangerous than one that is explicit about what it did not generate.
The scaffold output tells the engineer exactly what the generator produced and exactly what it left for them. That boundary, clearly expressed, is how generated code integrates into a real codebase without surprise.