NetChecker: Practice of Android Network Detection App Based on Clean Architecture

This article provides an in-depth analysis of the NetChecker project maintained by maryoa61 (released on GitHub, link: https://github.com/maryoa61/NetChecker-CleanArchitecture-Android, release date: 2026-06-09). This project uses the Clean Architecture pattern to build an Android network detection app, exploring modern Android development practices and architecture design with AI assistance. Core content includes key concepts of Clean Architecture, NetChecker's functions and technical implementation, AI applications in development, modern Android tech stack, testing strategies, and implementation suggestions, providing Android developers with references for architecture design and practice.

Android development has evolved over the past decade from single Activity files to MVC, MVP, MVVM, then to MVI and Clean Architecture. Clean Architecture was proposed by Robert C. Martin, with the core idea of separating business logic from framework details so that the system does not depend on specific technical implementations, thereby improving code modularity, testability, and maintainability. The NetChecker project demonstrates the practice of this architecture in Android apps.

Clean Architecture adopts a layered architecture (Domain Layer, Data Layer, Presentation Layer, Framework Layer) and follows core dependency rules: inner layers do not depend on outer layers (outer layers depend on inner layers, interface segregation, dependency injection). Its advantages include: testability (business logic independent of frameworks, fast JVM unit tests), maintainability (separation of responsibilities), replaceability (technical implementations can be replaced independently), and efficient team collaboration (parallel development).

NetChecker is a network status detection tool with functions including: connection status detection (internet connection and type), network quality assessment (latency, speed, packet loss rate), DNS resolution testing, port connectivity check, and historical record saving. User scenarios cover troubleshooting, network selection, monitoring and early warning, etc.

Domain Layer: Defines entities (e.g., NetworkStatus), use cases (e.g., CheckConnectivityUseCase), and Repository interfaces; Data Layer: Implements Repository interfaces, coordinates local (Room database) and remote (Retrofit/OkHttp) data sources; Presentation Layer: Uses ViewModel to manage UI data, StateFlow/LiveData to implement one-way data flow, Jetpack Compose or XML to build UI; Dependency Injection: Uses Hilt to manage component dependencies.

The project mentions 'Powered by artificial intelligence'. The roles of AI in development include: code generation (boilerplate code, unit tests, document comments), architecture suggestions (design pattern recommendations, code reviews, refactoring suggestions), and learning assistance (concept explanations, best practice sharing, problem solving).

Tech Stack: Programming languages (Kotlin, Coroutines, Flow), UI (Jetpack Compose, Material Design3), architecture components (ViewModel, Navigation, WorkManager), data storage (Room, DataStore), network communication (Retrofit, OkHttp, Kotlin Serialization). Testing Strategy: Unit tests (Domain Layer, Data Layer, Presentation Layer), integration tests (database, network), UI tests (Compose tests, Espresso end-to-end tests).

Implementation Suggestions: Organize project structure by Data Layer, Domain Layer, Presentation Layer, and DI modules; Modularization suggestions (feature modules, core modules, dynamic feature modules); Performance optimization (memory management, background tasks, battery optimization). Conclusion: Clean Architecture is an effective strategy to handle application complexity, AI is an auxiliary tool, and developers need to understand the underlying principles; It is recommended to practice from simple projects and gradually master the key points of the architecture.