A React Native component library for implementing A/B tests with experiment and variant components.
react-native-ab is a React Native library that enables A/B testing through reusable components. It solves the problem of implementing controlled experiments in mobile apps by providing an Experiment component to wrap variants and a Variant component to define alternative content, with built-in randomization and tracking callbacks.
React Native developers who need to run A/B tests to optimize user interfaces, features, or onboarding flows in their mobile applications.
Developers choose react-native-ab because it offers a simple, declarative API that integrates seamlessly with React Native, eliminating the need for complex state management or external services just to set up basic experiments.
A component for rendering A/B tests in React Native
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Uses <Experiment> and <Variant> components to define tests directly in JSX, making setup intuitive and eliminating complex state management for basic UI experiments.
Provides onChoice and onRawChoice callbacks to easily send experiment data to services like Google Analytics, as shown in the README with console logging examples.
Offers methods like reset() and getActiveVariant() accessible via refs, allowing dynamic management such as resetting tests on user actions, demonstrated in the usage example.
Defaults to random variant selection using Math.random(), with a customizable choose prop for overriding with custom logic, keeping basic tests straightforward.
Only handles A/B tests in the UI layer; experiments involving backend changes require separate implementation, as the library focuses on React Native components.
Lacks built-in support for advanced capabilities like multi-armed bandits, statistical significance tracking, or remote configuration, limiting use to simple A/B tests.
Relies on React refs for method access, which may not align well with modern functional components and hooks, adding complexity for teams using newer React patterns.
The README is minimal, with no examples for error handling, testing, or scaling to multiple experiments, which could hinder adoption in production environments.