Testing AI-Generated Android Apps: A Pragmatic Strategy

Testing AI-Generated Android Apps: A Pragmatic Strategy When you use AI to generate Android apps—whether through Claude, Codex, or other tools—you inherit both a gift and a responsibility. The gift is rapid prototyping. The responsibility is quality assurance. This guide covers a pragmatic testing strategy that prevents your AI-generated Kotlin apps from becoming unmaintainable nightmares. Why AI Apps Need Different Testing AI-generated code excels at scaffolding and boilerplate but often struggles with edge cases and domain logic. Your testing pyramid needs to be inverted compared to manually-written apps: Unit Tests (70%) : Test the logic AI can't infer (business rules, ViewModel state transitions, data transformations) Integration Tests (20%) : Test Room DAO layers, API clients, and data flow UI Tests (10%) : Test layouts and navigation (AI is decent at UI code) The key insight: AI-generated UI code is usually correct. AI-generated business logic needs skepticism. Testing Pyramid fo