Migrating Android Apps from Java to Kotlin

We see teams with tens of thousands of lines of Java getting stuck on the transition to Kotlin. Google officially announced that new Jetpack APIs—Paging 3, DataStore, WorkManager with coroutines, Jetpack Compose—have a Kotlin-first surface Google's Kotlin-first initiative. Technically they can be called from Java, but with so many adapters and workarounds that productivity drops. In a typical project, 30% of code is getters, setters, and null-checks; during migration to Kotlin, that volume shrinks 2–3 times. But Android Studio's auto-converter gives only a syntactic translation, not an architectural one. We help you migrate without stopping development and without losing stability. Contact us for a preliminary work estimate.

Why You Can't Just Click "Convert Java File to Kotlin"

Android Studio can convert Java files to Kotlin automatically. The result technically compiles. But it's not Kotlin; it's a transliteration of Java into Kotlin syntax:

• var everywhere instead of val — no immutability
• !! on every null reference — NullPointerException is just renamed to KotlinNullPointerException
• No data classes — the same POJOs with getters through field.get()
• object and companion objects are absent — static methods float around as extensions
• No coroutines — AsyncTask or RxJava remain
• Lambdas look like Java 8 lambdas, but without SAM conversion for custom interfaces

Such code gives none of Kotlin's advantages, only adds confusion. The auto-converter is a tool to start, not to finish.

What a Proper Migration Looks Like

Inventory Before You Start

The first step is a full audit of the codebase: number of classes by type (Activity, Fragment, ViewModel, Repository, Model, Util), test coverage, list of actively developed modules vs. stable ones, dependencies on Kotlin-incompatible patterns (e.g., finalize(), certain patterns with static inner classes).

Based on the audit, we build a plan: which files to convert first, which to touch last, and where parallel development on Java continues during migration.

Bottom-Up Strategy

Start with classes that have no Android dependencies: data models, utilities, constants. A Java POJO with fields, getters, and setters becomes a Kotlin data class — instant benefit: equals(), hashCode(), toString(), copy() for free.

// Before: Java POJO, 60 lines with getters/setters
// After:
data class UserProfile(
    val id: Long,
    val name: String,
    val email: String,
    val avatarUrl: String? = null
)

Then move to the Repository layer. The key decision here is how to handle async code. If the project used RxJava, there are two paths: keep RxJava (Kotlin works great with RxJava) or migrate to coroutines + Flow. The second path is strategically better but more expensive in the moment. For actively developed repositories, we go with coroutines; for stable modules without changes, we leave RxJava until the next major refactoring.

Migrating from LiveData to StateFlow

ViewModel layer: LiveData → StateFlow + SharedFlow. This is not mandatory; LiveData works in Kotlin too, but StateFlow behaves more predictably — no magic with LifecycleOwner, no observeForever leaks, no setValue vs postValue confusion. The replacement happens in three steps: change the field type, adapt subscriptions in fragments, update tests.

Activity and Fragment are migrated last. They have the most dependencies, the most legacy code, and errors there are the most costly.

Handling Java-Kotlin Interop

Until migration is complete, Java and Kotlin classes live side by side. Kotlin calls Java without issues. Java calls Kotlin — annotations are needed:

• @JvmStatic for companion object methods needed from Java
• @JvmField for fields without getters
• @JvmOverloads for functions with default parameters
• @Throws(IOException::class) if a Kotlin function throws checked exceptions

Ignoring these annotations is a common reason why auto-converted code doesn't compile from neighboring Java files.

Testing During Migration

Every converted class must pass existing tests unchanged — this guarantees the conversion didn't break logic. If tests were missing, this is the moment to write them, before conversion, while the logic is still clear from Java code. We use JUnit5 + MockK (for Kotlin classes) or Mockito (if Java test compatibility is needed).

CI must run tests on every PR. Migration without CI is chaos: you can't track which commit broke logic.

class UserRepository(private val api: UserApi) {
    suspend fun getUser(id: Long): Result<User> = runCatching {
        api.getUser(id)
    }
}

What Else Changes Along the Way

During migration, it makes sense to address accumulated technical debt: replace AsyncTask (deprecated since API 30) with coroutines, migrate from SharedPreferences to DataStore, update Retrofit to version with Kotlin suspend functions instead of Call<T>.

But "along the way" doesn't mean "all at once". Each such change risks regression. We compile an explicit list of "what we do within the migration"; everything else goes into the backlog for upcoming sprints.

Module Priority for Migration

What's Included in the Migration Work

• Codebase audit: volume assessment, test coverage, complex spots
• Staged conversion plan with priorities and timelines
• Writing tests for critical modules before conversion
• Manual architectural improvement (coroutines, data classes, null safety)
• Training the team in Kotlin patterns and new APIs
• Post-migration support: code review, interop refinement, performance optimization

Timelines

They depend on codebase size, test coverage, and whether parallel feature development is ongoing.

The estimate is refined after the audit. Cost is calculated individually.

Migration is an investment. A team working on Kotlin with coroutines and StateFlow closes tasks faster than the same team on Java with RxJava. Not because Kotlin is magically better, but because there's less boilerplate, better analysis tools (KSP vs KAPT, Kotlin lint rules), and the library ecosystem no longer resists. Write to us for a specialist consultation and a preliminary project estimate.

We are a team with 7 years of Android development experience, having completed over 30 Java-to-Kotlin migration projects. Order a code audit and we will prepare a detailed transition plan.

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.