Open Source Apple iPhone Visual Regression Testing Tools

React Native Owl is a visual regression testing library for React Native that enables developers to introduce visual regression tests to their apps for iOS and Android. Being heavily inspired by Detox, an end-to-end testing and automation framework, this library uses a similar API that makes setting up react-native-owl and running the tests locally and on your preferred CI service seamless. While Detox focuses on end-to-end testing in general, React Native Owl focuses on visual regression testing and helping you catch unexpected changes to the visual appearance of your app over time.

There are a few visual regression tools for the web, but most either cannot be run headless or use phantomjs which is deprecated and a browser nobody is actually using. They usually also require you to maintain fixtures. With react-native it's now possible to target multiple platforms with a single code base, but there's no single tool to test all to my knowledge. Loki aims to have easy setup, no to low maintenance cost, reproducible tests independent of which OS they are run on, runnable on CI, and support all platforms storybook does.

A library for isolated developing UI components and automatically taking snapshots of them. Playbook is a library that provides a sandbox for building UI components without having to worry about application-specific dependencies, strongly inspired by Storybook for JavaScript in web-frontend development. Components built by using Playbook can generate a standalone app as living style guide. This allows you to not only review UI quickly but also deliver more robust designs by separating business logics out of components. Besides, snapshots of each component can be automatically generated by unit tests, and visual regression testing can be performed using arbitrary third-party tools. For complex modern app development, it’s important to catch UI changes more sensitively and keep improving them faster. With the Playbook, you don't have to struggle through preparing the data and spend human resources for manual testings.