Setting Up Dependency Injection (Dagger 2) in Android App

We often encounter projects where Dagger 2 is added midway through development. The result is a tangled graph, memory leaks, and bugs that cannot be reproduced locally. Dagger generates code at compile time — no reflection, but this safety comes at the cost of architectural discipline. Our experience: setup from scratch takes 3–5 days, and refactoring a chaotic implementation takes from a week. With over 5 years of work, we have set up Dagger on 30+ projects of various scales — from startups to enterprise applications. We guarantee clean DI code without runtime surprises. Get a consultation for your project: order an analysis of your current architecture.

How to properly design a component graph?

The typical schema for a large app: AppComponent (Singleton) → ActivityComponent (PerActivity) → FragmentComponent (PerFragment). Each level is a subcomponent or dependent component. The architectural mistake is placing all dependencies in AppComponent, which increases build times and complicates testing.

@Singleton
@Component(modules = [AppModule::class, NetworkModule::class, DatabaseModule::class])
interface AppComponent {
    fun inject(app: App)
    fun activityComponentBuilder(): ActivityComponent.Builder
}

@Module
class NetworkModule {
    @Provides
    @Singleton
    fun provideOkHttpClient(): OkHttpClient {
        return OkHttpClient.Builder()
            .addInterceptor(AuthInterceptor())
            .connectTimeout(30, TimeUnit.SECONDS)
            .build()
    }

    @Provides
    @Singleton
    fun provideRetrofit(client: OkHttpClient): Retrofit {
        return Retrofit.Builder()
            .baseUrl(BuildConfig.API_URL)
            .client(client)
            .addConverterFactory(GsonConverterFactory.create())
            .build()
    }
}

Why are scopes a common cause of elusive bugs?

The most frequent mistake is incorrect scopes. If UserRepository is declared @Singleton and AuthToken inside it is stored in memory, then after logout, without recreating the component, the old token remains alive. This leads to requests with a stale token — a production bug that only reproduces under a specific scenario. Solution: @Singleton components should not contain mutable state that depends on the user session. Session-scoped dependencies should be moved to @UserScope:

@Scope
@Retention(AnnotationRetention.RUNTIME)
annotation class UserScope

@UserScope
@Subcomponent(modules = [UserModule::class])
interface UserComponent {
    @Subcomponent.Factory
    interface Factory {
        fun create(@BindsInstance userId: String): UserComponent
    }

    fun inject(profileFragment: ProfileFragment)
}

UserComponent is created after login and destroyed after logout. All dependencies bound to the user live exactly as long as needed. Below is a table of typical mistakes:

Additionally, improper use of scopes can cause memory leaks due to accidentally holding an Activity context in @Singleton. We always check the graph for such scenarios.

Multibindings and plugin architecture

@IntoMap with @ViewModelKey is a pattern for injecting ViewModels via ViewModelProvider.Factory. Dagger creates a Map<Class<out ViewModel>, Provider<ViewModel>>, and the factory selects the correct class. Without this pattern, each ViewModel must be declared separately in the component.

@Module
abstract class ViewModelModule {
    @Binds
    @IntoMap
    @ViewModelKey(LoginViewModel::class)
    abstract fun bindLoginViewModel(vm: LoginViewModel): ViewModel

    @Binds
    @IntoMap
    @ViewModelKey(ProfileViewModel::class)
    abstract fun bindProfileViewModel(vm: ProfileViewModel): ViewModel
}

This same approach applies to plugin architecture, where each module registers its dependencies via @IntoSet or @IntoMap. We used this in a project with 10+ feature modules — Dagger automatically collects all provided implementations.

Kapt and KSP: which to choose for builds?

Dagger 2 traditionally works with kapt. Starting from version 2.50, experimental support for KSP is available, which speeds up incremental builds. On a project with ~200 Dagger annotations, switching from kapt to KSP reduced clean build time from 4.5 to 2.8 minutes — a significant development time saving.

// build.gradle.kts
plugins {
    id("com.google.devtools.ksp")
}

dependencies {
    implementation("com.google.dagger:dagger:2.51")
    ksp("com.google.dagger:dagger-compiler:2.51")
}

Also, don't forget about obfuscation: ProGuard/R8 can remove classes generated by Dagger if keep rules are not added. We include this in the configuration.

How to test an app with Dagger?

Dagger and tests are a separate story. The standard approach: test modules replace production dependencies with fakes:

@Component(modules = [TestNetworkModule::class, DatabaseModule::class])
interface TestAppComponent : AppComponent

@Module
class TestNetworkModule {
    @Provides
    @Singleton
    fun provideApiService(): ApiService = FakeApiService()
}

In Espresso tests, DaggerTestAppComponent is substituted for the main one in App.appComponent before the test runs. Without this replacement, integration tests hit the real server. We also use TestCoroutineDispatcher to simulate delays.

When to choose Dagger 2 over Hilt

Hilt is a wrapper over Dagger with a predefined component structure. If you need custom scopes, multi-module graphs with independent components, or Dagger is already in the project — Dagger 2 gives full control. Hilt gets you started faster, but limits complex architectures. In our projects, we often combine Dagger with Hilt in different modules.

What is included in a turnkey Dagger 2 setup

• Analysis of architecture and component graph design
• Creation of modules for network, database, shared preferences
• Scope configuration (Singleton, PerActivity, UserScope)
• ViewModel integration via multibinding
• Test component setup with fakes
• Migration from kapt to KSP if needed
• Documentation for graph maintenance

The cost is calculated individually. Setup from scratch — 3–5 days, refactoring — from a week. Get a consultation: write to us, we'll assess your project. Want to implement Dagger 2 without headaches? Contact us, we'll audit your DI code.

Why is native Android development with Kotlin the production standard?

RecyclerView with DiffUtil.calculateDiff() on main thread, a list of 500 items, an average older Android phone – the user gets 200–400 ms freezes on every data update. Move the diff calculation to a background thread via AsyncListDiffer – the problem disappears. These things aren't obvious without a profiler and understanding Android’s threading model. According to Wikipedia (Android development), improper threading is one of the top causes of ANRs. We encounter such pitfalls daily, so our team bakes profiling and optimization into every sprint. One day of downtime due to ANR can cost an app with 100 000 DAU significant revenue losses – refactoring threading pays off within a week.

Kotlin + Jetpack Compose + Coroutines is the current production standard for native Android development. XML and View system haven’t disappeared, but we start new projects only with Compose. The result: fewer bugs, faster iterations, 30% less code compared to the classic approach. Want to estimate savings on your project? Contact us – we’ll do a free code audit within half a day.

How does recomposition work in Jetpack Compose and why is it important?

Compose is a declarative UI framework. Instead of TextView.setText() and adapter.notifyItemChanged() – composable functions that describe UI as a function of state. When state changes, Compose recomputes only the affected parts of the tree. This is called recomposition.

Problem: recomposition can be too frequent. If you pass a lambda created on every recomposition of the parent to a composable, the child composable will recompose every time, even if the visible data hasn’t changed.

// Bad – new lambda on each recomposition, child component thinks parameter changed
@Composable
fun ParentScreen(viewModel: MyViewModel = hiltViewModel()) {
    val items by viewModel.items.collectAsState()
    ItemList(
        items = items,
        onItemClick = { id -> viewModel.selectItem(id) } // created anew each time
    )
}

// Good – remember stabilizes the lambda
@Composable
fun ParentScreen(viewModel: MyViewModel = hiltViewModel()) {
    val items by viewModel.items.collectAsState()
    val onItemClick = remember { { id: String -> viewModel.selectItem(id) } }
    ItemList(items = items, onItemClick = onItemClick)
}

Stability and @Stable/@Immutable

Compose determines whether to recompose a composable by checking the stability of parameters. A type is considered stable if Compose can guarantee: if two values are equal by equals(), their UI representation is the same.

Primitives, String, data classes with val fields of stable types are automatically stable. List<T> is unstable because it’s an interface. MutableList can change without notification. Solution: use ImmutableList from kotlinx.collections.immutable or annotate a data class with @Immutable.

// List<Item> is unstable – LazyColumn will recompose excessively
@Composable
fun ItemList(items: List<Item>) { ... }

// ImmutableList is stable – Compose skips recomposition if items haven't changed
@Composable
fun ItemList(items: ImmutableList<Item>) { ... }

For diagnosing recomposition issues we use Compose Compiler Metrics. Add flags -P plugin:androidx.compose.compiler.plugins.kotlin:reportsDestination=... to build.gradle and get a report: which composables are restartable, which are skippable, why a parameter is unstable.

LazyColumn and list performance

LazyColumn is the RecyclerView equivalent in Compose. key in items { } is mandatory for any list where items can move or be deleted. Without key, Compose cannot distinguish moving an item from deleting one and adding another, breaking animations and potentially causing unexpected cell state reset.

LazyColumn {
    items(
        items = messages,
        key = { message -> message.id } // stable identifier
    ) { message ->
        MessageItem(message = message)
    }
}

contentType is an additional optimization. With multiple cell types, Compose can reuse composition for cells of the same type. It’s analogous to getItemViewType in RecyclerView.

How to avoid common mistakes when using coroutines?

Coroutines are structured concurrency with a clear scope and lifecycle.

viewModelScope is a coroutine scope tied to the ViewModel lifecycle. When the ViewModel is cleared (onCleared()), all coroutines in the scope are automatically cancelled. This eliminates a whole class of leaks typical for callback-based approaches.

@HiltViewModel
class OrderViewModel @Inject constructor(
    private val orderRepository: OrderRepository
) : ViewModel() {

    private val _uiState = MutableStateFlow<OrderUiState>(OrderUiState.Loading)
    val uiState: StateFlow<OrderUiState> = _uiState.asStateFlow()

    fun loadOrder(orderId: String) {
        viewModelScope.launch {
            _uiState.value = OrderUiState.Loading
            try {
                val order = orderRepository.getOrder(orderId) // suspend function
                _uiState.value = OrderUiState.Success(order)
            } catch (e: IOException) {
                _uiState.value = OrderUiState.Error(e.message)
            }
        }
    }
}

What to choose: StateFlow or LiveData?

StateFlow and SharedFlow are the recommended replacements for LiveData in Kotlin projects. LiveData is lifecycle-aware but tied to the Android platform. Flow is pure Kotlin, testable without Android dependencies.

collectAsState() in Compose subscribes to StateFlow and triggers recomposition on new value. lifecycleScope.launch { flow.collect { } } is for collection in Fragment or Activity with lifecycle awareness via repeatOnLifecycle(Lifecycle.State.STARTED).

repeatOnLifecycle is important. Without it, the flow will be collected even when the app is in the background, potentially causing UI event processing when the window is not active. Apps that ignore this see up to 40% more battery drain and missed UI updates.

Dispatchers and structured concurrency

Dispatchers.IO for network requests and file operations. Dispatchers.Default for CPU-intensive tasks (parsing, sorting, encryption). Dispatchers.Main for UI.

withContext(Dispatchers.IO) switches the coroutine to the appropriate dispatcher without creating a new scope. This is more efficient than launch(Dispatchers.IO) inside another launch.

// Correct pattern in Repository
suspend fun getOrders(): List<Order> = withContext(Dispatchers.IO) {
    orderDao.getAll() // Room automatically suspend, but explicit IO dispatcher is good practice
}

Hilt and dependency injection

Hilt is the official DI framework for Android built on top of Dagger 2. It eliminates Dagger boilerplate: no need to write Component and manually connect Module with Component.

@HiltViewModel + @Inject constructor – ViewModel with dependency injection without factories. @Singleton, @ActivityScoped, @ViewModelScoped – proper lifecycle for dependencies.

A common mistake: using @Singleton for a repository that holds an Activity context. This leaks the Activity. Rule: @Singleton only for dependencies that need Application context or don’t store Android-specific state.

Want to implement DI without headaches? Contact us – we’ll set up Hilt within an hour on any existing project.

WorkManager and background tasks

WorkManager for guaranteed background tasks that must execute even after app or device restart. Data sync, analytics upload, file downloads.

CoroutineWorker is the suspend version of Worker. It runs on Dispatchers.IO by default.

Android 14 tightened background execution requirements. FOREGROUND_SERVICE_TYPE is mandatory for foreground services. WorkManager correctly handles constraints (network, charging) and doesn’t require foreground service for most tasks.

Tools

Android Studio Profiler – CPU profiler with System Trace shows everything: coroutine suspension points, RenderThread, MainThread. Memory profiler – heap dump, allocation tracking. Network profiler – all HTTP requests with bodies.

Compose Layout Inspector – composable tree with recomposition counts. Shows which composables recompose too often – more precise than any logging.

LeakCanary – automatic memory leak detection in development builds. Shows reference chain to the leak. Added with one dependency, works without configuration.

Firebase Crashlytics + Performance Monitoring – crash-free rate by version, network request traces, custom traces for critical operations.

What’s included in native Android development: our process

1. Requirements audit and architecture design – diagrams, stack selection, prototype.
2. Implementation with Kotlin + Jetpack Compose – StateFlow, Hilt, Coroutines, Navigation.
3. Backend integration – REST/GraphQL, WebSocket, push notifications (FCM), Android App Links.
4. Testing – unit tests (JUnit, MockK) with 85%+ coverage, UI tests (Compose Test), load testing.
5. CI/CD – GitHub Actions / GitLab CI with automated builds, linters, and publication to Google Play Console.
6. Documentation – README, ADR (Architecture Decision Records), code comments.
7. Post-release support – monitoring, crashlytics, hotfixes, updates.
8. Code warranty – 3 months of free support after delivery.

From real projects we’ve seen: missing key in LazyColumn causes broken animations and binding resets; @Singleton repository with Activity context leads to memory leaks; flows collected without repeatOnLifecycle process events in background; using Dispatchers.Main for IO results in ANR; unstable types in Compose cause excessive list recomposition; manual cache management without Room or DataStore creates chaos. After refactoring these issues, clients report a 40% reduction in crash rate within the first month, and API response time drops from 1200 ms to 400 ms due to proper dispatcher handling and caching.

Timelines

Cost is calculated after requirements analysis and specification. Estimate is free. Get a consultation – we’ll prepare a detailed commercial proposal with stage breakdown.

Why trust us

5+ years on the market, 70+ completed Android projects (from startups to enterprise). Our team includes a Lead Android Developer with experience at Google and Associate Android Developer certification. All projects undergo Code Review with Checkstyle and Detekt, ensuring code quality. For production builds, we use ProGuard/R8 with custom shrink rules, reducing APK size by 25–35% without loss of functionality. With us you get a predictable result – contact us to see how your app can improve.