Android现代化技术栈剖析：架构、AI与工程化实践
基于设备能力、网络状态、任务复杂度的智能路由算法，端侧AI与云端AI协同合作，运用隐私敏感的AI计算策略，在隐私和功能间取得平衡。将MVVM、Clean Architecture、MVI整合，将单向数据流与响应式编程，确保状态管理的可预测，可调试。完整的模块化开发、测试、部署流水线编程，从代码编写到性能监控，实现了多环境的灵活配置管理。使用启动、内存、网络、渲染优化方案，通过智能优化决策系统进行性
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目录
三、架构模式：MVVM + Clean Architecture + MVI的融合
四、AI集成：TensorFlow Lite + 云服务的混合架构
一、Kotlin为主，Java为辅的现代化语言策略
1.1 Kotlin语言特性深度应用、
这部分主要解决多语言环境下代码的兼容性和现代化转型问题，Kotlin具有协程优化异步处理的功能，查看官网资料和询问AI后，发现可以利用Kotlin DSL简化复杂配置，同时保持与Java代码的无缝互操作。
实现原理：
通过CoroutineScope管理协程生命周期，避免内存泄漏；使用async/await实现网络请求的并行处理；用Flow替代LiveData，扩展函数和lambda表达式创建领域特定语言；通过@JvmOverloads、@JvmStatic等注解确保Java使用。
技术创新点：
自动取消未完成请求，数据库可以与网络数据的自动同步，渐进式Java到Kotlin迁移策略，最小化风险。
// 1.1.1 协程与结构化并发
class UserRepository @Inject constructor(
private val userDao: UserDao,
private val apiService: UserApiService
) {
// 使用CoroutineScope管理生命周期
private val scope = CoroutineScope(SupervisorJob() + Dispatchers.IO)
// 并发优化：并行网络请求
suspend fun fetchUserData(userId: String): UserData = coroutineScope {
val userDeferred = async { apiService.getUser(userId) }
val settingsDeferred = async { apiService.getUserSettings(userId) }
val historyDeferred = async { apiService.getUserHistory(userId) }
val user = userDeferred.await()
val settings = settingsDeferred.await()
val history = historyDeferred.await()
// 合并数据
UserData(user, settings, history).also { data ->
// 并发写入数据库
launch { userDao.insertOrUpdate(data) }
}
}
// 1.1.2 Flow状态流管理
fun observeUserData(userId: String): Flow<UserData> = channelFlow {
// 数据库与网络的同步
val localFlow = userDao.observeUser(userId)
val networkFlow = flow {
val data = fetchUserData(userId)
emit(data)
}
// 合并流：优先显示本地，网络更新后刷新
merge(
localFlow,
networkFlow
)
.distinctUntilChanged()
.collect { send(it) }
}
// 1.1.3 Kotlin DSL构建配置
fun buildComplexConfiguration() = complexConfig {
network {
baseUrl = "https://api.example.com"
connectTimeout = 30.seconds
readTimeout = 30.seconds
loggingInterceptor {
level = if (BuildConfig.DEBUG) Level.BODY else Level.NONE
}
}
database {
name = "app_database.db"
version = 1
exportSchema = true
}
ai {
engineType = EngineType.HYBRID
cacheStrategy = CacheStrategy.LRU
}
}
}
// DSL构建器
class ComplexConfigDsl {
var network: NetworkConfig? = null
var database: DatabaseConfig? = null
var ai: AIConfig? = null
fun network(block: NetworkConfigDsl.() -> Unit) {
network = NetworkConfigDsl().apply(block).build()
}
fun database(block: DatabaseConfigDsl.() -> Unit) {
database = DatabaseConfigDsl().apply(block).build()
}
}1.2 Java兼容性处理策略
// 1.2.1 向后兼容的Java接口
public interface LegacyCallback {
void onSuccess(Result result);
void onError(Throwable error);
}
// Kotlin端的适配器
class CallbackAdapter(
private val legacyCallback: LegacyCallback
) {
suspend fun <T> adapt(
block: suspend () -> T
): T = suspendCoroutine { continuation ->
try {
val result = block()
legacyCallback.onSuccess(result.toLegacyResult())
continuation.resumeWith(Result.success(result))
} catch (e: Exception) {
legacyCallback.onError(e)
continuation.resumeWith(Result.failure(e))
}
}
}
// 1.2.2 Java与Kotlin互操作优化
@JvmOverloads
fun processData(
data: String,
options: ProcessingOptions = ProcessingOptions.default(),
@Nullable callback: ((Result) -> Unit)? = null
) {
// 处理逻辑
}
// 生成Java友好的静态方法
object FileUtils {
@JvmStatic
fun readFile(path: String): String = File(path).readText()
@JvmStatic
fun writeFile(path: String, content: String) {
File(path).writeText(content)
}
}二、声明式UI：Jetpack Compose深度实践
代码作用：
构建现代化、高性能的声明式用户界面，通过状态驱动UI更新，优化用户体验。
实现原理：
单向数据流确保状态可预测，通过状态变化自动触发UI更新，基于状态的声明式动画。
技术创新点：
单一数据源确保UI一致性，通过DerivedStateOf减少重绘，Canvas API实现高性能自定义绘制组件。
2.1 现代化UI架构模式
// 2.1.1 Compose + MVI状态管理
data class MainScreenState(
val userData: UserData? = null,
val predictions: List<Prediction> = emptyList(),
val isLoading: Boolean = false,
val error: UiError? = null,
val searchQuery: String = "",
val selectedTab: MainTab = MainTab.HOME
) {
val filteredPredictions: List<Prediction>
get() = predictions.filter { prediction ->
searchQuery.isEmpty() || prediction.title.contains(searchQuery, ignoreCase = true)
}
}
sealed class MainScreenEvent {
data class SearchQueryChanged(val query: String) : MainScreenEvent()
data class TabSelected(val tab: MainTab) : MainScreenEvent()
object LoadData : MainScreenEvent()
data class PredictionClicked(val id: String) : MainScreenEvent()
object Retry : MainScreenEvent()
}
sealed class MainScreenSideEffect {
data class NavigateToDetail(val predictionId: String) : MainScreenSideEffect()
data class ShowSnackbar(val message: String) : MainScreenSideEffect()
data class ShowDialog(val type: DialogType) : MainScreenSideEffect()
}
@HiltViewModel
class MainViewModel @Inject constructor(
private val getUserDataUseCase: GetUserDataUseCase,
private val getPredictionsUseCase: GetPredictionsUseCase
) : ViewModel() {
private val _state = MutableStateFlow(MainScreenState())
val state: StateFlow<MainScreenState> = _state.asStateFlow()
private val _sideEffects = Channel<MainScreenSideEffect>()
val sideEffects: Flow<MainScreenSideEffect> = _sideEffects.receiveAsFlow()
fun handleEvent(event: MainScreenEvent) {
when (event) {
is MainScreenEvent.SearchQueryChanged -> {
_state.update { it.copy(searchQuery = event.query) }
}
is MainScreenEvent.TabSelected -> {
_state.update { it.copy(selectedTab = event.tab) }
}
MainScreenEvent.LoadData -> loadData()
is MainScreenEvent.PredictionClicked -> {
viewModelScope.launch {
_sideEffects.send(
MainScreenSideEffect.NavigateToDetail(event.id)
)
}
}
MainScreenEvent.Retry -> {
_state.update { it.copy(error = null) }
loadData()
}
}
}
private fun loadData() {
_state.update { it.copy(isLoading = true) }
viewModelScope.launch {
val userDeferred = async { getUserDataUseCase() }
val predictionsDeferred = async { getPredictionsUseCase() }
try {
val userData = userDeferred.await()
val predictions = predictionsDeferred.await()
_state.update {
it.copy(
userData = userData.getOrNull(),
predictions = predictions.getOrElse { emptyList() },
isLoading = false
)
}
} catch (e: Exception) {
_state.update {
it.copy(
error = UiError.fromException(e),
isLoading = false
)
}
}
}
}
}2.2 Compose性能优化实践
// 2.2.1 使用DerivedStateOf和remember优化重组
@Composable
fun PredictionList(
predictions: List<Prediction>,
searchQuery: String
) {
val filteredPredictions = remember(predictions, searchQuery) {
derivedStateOf {
predictions.filter { prediction ->
searchQuery.isEmpty() ||
prediction.title.contains(searchQuery, true) ||
prediction.content.contains(searchQuery, true)
}
}
}
LazyColumn {
items(
items = filteredPredictions.value,
key = { prediction -> prediction.id }
) { prediction ->
// 使用stickyHeaders快速滚动
if (prediction.shouldShowDateHeader) {
StickyHeader {
DateHeader(prediction.createdAt)
}
}
PredictionItem(
prediction = prediction,
modifier = Modifier.animateItemPlacement() // 动画优化
)
}
}
}
// 2.2.2 自定义布局与绘制
@Composable
fun WaveformVisualizer(
amplitudeData: List<Float>,
modifier: Modifier = Modifier
) {
Canvas(modifier = modifier.fillMaxWidth().height(100.dp)) {
val barWidth = size.width / amplitudeData.size
val centerY = size.height / 2
amplitudeData.forEachIndexed { index, amplitude ->
val x = index * barWidth
val barHeight = amplitude * size.height
drawRect(
color = Color.Blue,
topLeft = Offset(x, centerY - barHeight / 2),
size = Size(barWidth - 2.dp.toPx(), barHeight)
)
}
}
}
// 2.2.3 复杂手势处理
@Composable
fun DraggablePredictionCard(
prediction: Prediction,
onDismiss: () -> Unit
) {
val offsetX = remember { Animatable(0f) }
val dismissThreshold = LocalConfiguration.current.screenWidthDp.dp * 0.4f
Box(
modifier = Modifier
.fillMaxWidth()
.pointerInput(Unit) {
detectHorizontalDragGestures(
onDragEnd = {
if (abs(offsetX.value) > dismissThreshold.toPx()) {
animateDismissal()
} else {
offsetX.animateTo(0f)
}
}
) { change, dragAmount ->
offsetX.snapTo(offsetX.value + dragAmount)
}
}
) {
Card(
modifier = Modifier.offset { IntOffset(offsetX.value.roundToInt(), 0) }
) {
// 卡片内容
}
}
}三、架构模式：MVVM + Clean Architecture + MVI的融合
代码作用：
实现可测试、可维护、可扩展的应用架构，分离业务逻辑、数据层和表现层。
实现原理：
Clean Architecture的依赖规则是从内向外，采用实体、值对象、领域服务、仓储模式，从事件到状态更新再到UI渲染，最后进行单元测试、集成测试、端到端测试的分层测试。
技术创新点：
采用MVVM+Clean Architecture+MVI编程，具有本地优先，分级错误处理和优雅降级的功能，而且功能模块独立开发和测试。
3.1 Clean Architecture实现细节
// 3.1.1 领域层 - 纯粹的Kotlin/JVM模块
// :domain模块的build.gradle.kts
plugins {
id("kotlin")
id("kotlin-kapt")
}
dependencies {
implementation(libs.kotlin.stdlib)
implementation(libs.kotlin.coroutines.core)
// 不依赖任何Android框架
testImplementation(libs.junit)
testImplementation(libs.mockk)
}
// 领域实体
data class User(
val id: String,
val name: String,
val email: String,
val preferences: UserPreferences
) {
val isPremium: Boolean
get() = preferences.subscriptionType == SubscriptionType.PREMIUM
fun canAccessFeature(feature: Feature): Boolean {
return when (feature) {
Feature.BASIC -> true
Feature.ADVANCED -> isPremium
Feature.AI -> preferences.enableAI && isPremium
}
}
}
// 领域用例
class GeneratePredictionUseCase @Inject constructor(
private val predictionRepository: PredictionRepository,
private val aiRouter: AIRouter,
private val validationService: ValidationService
) {
suspend operator fun invoke(
input: PredictionInput
): Result<Prediction> = withContext(Dispatchers.IO) {
// 输入验证
validationService.validate(input).fold(
onSuccess = { validatedInput ->
// 业务逻辑
val existingPrediction = predictionRepository.findSimilar(validatedInput)
if (existingPrediction != null) {
return@withContext Result.success(existingPrediction)
}
// AI处理
aiRouter.process(validatedInput.toAiInput()).map { aiResult ->
val prediction = Prediction(
id = UUID.randomUUID().toString(),
input = validatedInput,
result = aiResult,
createdAt = Instant.now(),
confidence = aiResult.confidence
)
// 保存
predictionRepository.save(prediction)
prediction
}
},
onFailure = { error ->
Result.failure(error)
}
)
}
}
// 3.1.2 数据层 - 多种数据源
class PredictionRepositoryImpl @Inject constructor(
private val localDataSource: PredictionLocalDataSource,
private val remoteDataSource: PredictionRemoteDataSource,
private val cacheDataSource: PredictionCacheDataSource,
private val networkMonitor: NetworkMonitor
) : PredictionRepository {
override fun getPredictionsStream(): Flow<List<Prediction>> = channelFlow {
// 优先从本地数据库获取
val localFlow = localDataSource.getPredictionsStream()
// 监听网络状态
networkMonitor.isOnline.collectLatest { isOnline ->
if (isOnline) {
// 网络请求
try {
val remotePredictions = remoteDataSource.getPredictions()
localDataSource.updatePredictions(remotePredictions)
} catch (e: Exception) {
// 网络失败，回退到缓存
val cached = cacheDataSource.getCachedPredictions()
send(cached)
}
}
// 发射本地数据
localFlow.collect { predictions ->
send(predictions)
}
}
}
override suspend fun savePrediction(prediction: Prediction) {
// 写入策略：先本地，后远程，使用缓存兜底
localDataSource.savePrediction(prediction)
cacheDataSource.cachePrediction(prediction)
if (networkMonitor.isCurrentlyOnline()) {
try {
remoteDataSource.savePrediction(prediction)
} catch (e: Exception) {
// 标记为待同步
localDataSource.markForSync(prediction.id)
}
}
}
}四、AI集成：TensorFlow Lite + 云服务的混合架构
代码作用：
实现端侧AI推理与云端AI服务的智能协同，平衡性能、隐私和功能。
实现原理：
动态加载、缓存、版本控制的代码处理流水线编程过程，多种路由策略，gRPC高性能通信，Protobuf序列化等技术实现网络、性能和精度多维度降级。
技术创新点：
端侧+云端的架构，基于设备性能和任务复杂度自动选择模型，将任务分割处理，敏感信息本地处理，其余交给云端，大大减轻系统压力。
4.1 TensorFlow Lite模型管理与推理
// 4.1.1 模型管理器
class ModelManager @Inject constructor(
@ApplicationContext private val context: Context,
private val assetManager: AssetManager,
private val fileManager: FileManager
) {
// 模型配置
data class ModelConfig(
val name: String,
val assetPath: String,
val inputShape: List<Int>,
val outputShape: List<Int>,
val quantized: Boolean = false,
val gpuDelegate: Boolean = false
)
// 支持的模型
private val supportedModels = mapOf(
ModelType.IMAGE_CLASSIFICATION to ModelConfig(
name = "mobilenet_v2",
assetPath = "models/mobilenet_v2.tflite",
inputShape = listOf(1, 224, 224, 3),
outputShape = listOf(1, 1001)
),
ModelType.OBJECT_DETECTION to ModelConfig(
name = "ssd_mobilenet",
assetPath = "models/ssd_mobilenet.tflite",
inputShape = listOf(1, 300, 300, 3),
outputShape = listOf(1, 10, 4),
gpuDelegate = true
),
ModelType.TEXT_GENERATION to ModelConfig(
name = "bert_qa",
assetPath = "models/bert_qa.tflite",
inputShape = listOf(1, 384),
outputShape = listOf(1, 384)
)
)
// 模型缓存
private val modelCache = LruCache<String, Interpreter>(MAX_CACHE_SIZE)
suspend fun loadModel(
modelType: ModelType,
options: Interpreter.Options = Interpreter.Options()
): Interpreter = withContext(Dispatchers.IO) {
val config = supportedModels[modelType] ?: throw ModelNotFoundException(modelType)
val cacheKey = "${modelType.name}_${config.name}"
// 从缓存获取
modelCache.get(cacheKey)?.let { return@withContext it }
// 加载模型
val modelFile = loadModelFile(config)
// 配置选项
options.numThreads = 4
if (config.gpuDelegate && isGpuDelegateAvailable()) {
GpuDelegate().also { delegate ->
options.addDelegate(delegate)
}
}
if (config.quantized) {
options.setAllowFp16PrecisionForFp32(true)
}
// 创建解释器
val interpreter = Interpreter(modelFile, options)
// 放入缓存
modelCache.put(cacheKey, interpreter)
interpreter
}
private suspend fun loadModelFile(config: ModelConfig): File = withContext(Dispatchers.IO) {
val cacheDir = context.cacheDir
val modelFile = File(cacheDir, config.name)
if (!modelFile.exists()) {
// 从assets复制
assetManager.open(config.assetPath).use { input ->
modelFile.outputStream().use { output ->
input.copyTo(output)
}
}
}
modelFile
}
// 4.1.2 推理管道
class InferencePipeline @Inject constructor(
private val modelManager: ModelManager,
private val preprocessor: Preprocessor,
private val postprocessor: Postprocessor
) {
suspend fun processImage(
bitmap: Bitmap,
modelType: ModelType = ModelType.IMAGE_CLASSIFICATION
): InferenceResult = withContext(Dispatchers.Default) {
val interpreter = modelManager.loadModel(modelType)
// 预处理
val inputTensor = preprocessor.processImage(bitmap, modelType)
// 推理
val outputTensor = Array(1) { FloatArray(OUTPUT_SIZE) }
val startTime = System.currentTimeMillis()
interpreter.run(inputTensor, outputTensor)
val inferenceTime = System.currentTimeMillis() - startTime
// 后处理
val result = postprocessor.processOutput(outputTensor[0], modelType)
InferenceResult(
result = result,
inferenceTime = inferenceTime,
modelType = modelType
)
}
}
}4.2 云AI服务集成
// 4.2.1 基于gRPC的高性能通信
class GrpcAIClient @Inject constructor(
private val okHttpClient: OkHttpClient,
private val gson: Gson
) : AIClient {
private val channel: ManagedChannel by lazy {
ManagedChannelBuilder
.forAddress(AI_SERVER_HOST, AI_SERVER_PORT)
.useTransportSecurity()
.maxInboundMessageSize(MAX_MESSAGE_SIZE)
.keepAliveTime(30, TimeUnit.SECONDS)
.keepAliveTimeout(10, TimeUnit.SECONDS)
.build()
}
private val stub: AIServiceGrpc.AIServiceStub by lazy {
AIServiceGrpc.newStub(channel)
.withInterceptors(
AuthInterceptor(authToken),
LoggingInterceptor(),
RetryInterceptor()
)
}
override suspend fun predict(
request: PredictionRequest
): Result<PredictionResponse> = suspendCoroutine { continuation ->
val call = stub.predict(
request.toProto(),
object : StreamObserver<PredictionResponseProto> {
override fun onNext(value: PredictionResponseProto) {
val response = value.fromProto()
continuation.resume(Result.success(response))
}
override fun onError(t: Throwable) {
continuation.resume(Result.failure(t))
}
override fun onCompleted() {
// 流式调用时使用
}
}
)
// 设置超时
channel.awaitTermination(REQUEST_TIMEOUT_SECONDS, TimeUnit.SECONDS)
}
}
// 4.2.2 智能降级策略
class IntelligentFallbackStrategy @Inject constructor(
private val connectivityManager: ConnectivityManager,
private val deviceInfoProvider: DeviceInfoProvider,
private val settingsManager: SettingsManager
) {
data class FallbackDecision(
val useLocalModel: Boolean,
val modelType: ModelType,
val quality: QualityLevel,
val reason: String
)
suspend fun decideStrategy(
input: AIInput,
availableModels: List<ModelType>
): FallbackDecision = withContext(Dispatchers.Default) {
val networkInfo = connectivityManager.activeNetworkInfo
val isConnected = networkInfo?.isConnectedOrConnecting == true
val isMetered = connectivityManager.isActiveNetworkMetered
val deviceCapabilities = deviceInfoProvider.getCapabilities()
val inputSize = estimateInputSize(input)
return@withContext when {
// 1. 无网络连接
!isConnected -> FallbackDecision(
useLocalModel = true,
modelType = availableModels.minBy { it.sizeInMB },
quality = QualityLevel.LOW,
reason = "No network connection"
)
// 2. 计量网络 + 大数据量
isMetered && inputSize > METERED_THRESHOLD -> FallbackDecision(
useLocalModel = true,
modelType = ModelType.IMAGE_CLASSIFICATION,
quality = QualityLevel.MEDIUM,
reason = "Large input on metered network"
)
// 3. 设备性能不足
deviceCapabilities.score < PERFORMANCE_THRESHOLD -> FallbackDecision(
useLocalModel = false,
modelType = ModelType.CLOUD_OPTIMIZED,
quality = QualityLevel.HIGH,
reason = "Device performance constraints"
)
// 4. 用户设置优先
settingsManager.useLocalAIOnly -> FallbackDecision(
useLocalModel = true,
modelType = availableModels.firstOrNull() ?: ModelType.DEFAULT,
quality = QualityLevel.MEDIUM,
reason = "User preference"
)
// 5. 默认使用云端
else -> FallbackDecision(
useLocalModel = false,
modelType = ModelType.CLOUD_FULL,
quality = QualityLevel.HIGH,
reason = "Optimal conditions"
)
}
}
}五、构建工具与模块化架构
代码作用：
实现高效的构建流程、模块化开发和持续集成。
实现原理：
构建类型安全的脚本和各类功能模块，共享库等，支持多种环境处理，做到资源压缩、Dex优化。
技术创新点：
通过convention插件标准化所有模块配置，Flavor自动配置依赖和资源，按需加载后节省包体大小，加快编译速度。
5.1 现代化Gradle配置
// 项目级 build.gradle.kts
plugins {
id("com.android.application") version "8.1.0" apply false
id("com.android.library") version "8.1.0" apply false
id("org.jetbrains.kotlin.android") version "1.9.0" apply false
id("com.google.dagger.hilt.android") version "2.48" apply false
id("com.google.devtools.ksp") version "1.9.0-1.0.13" apply false
}
// 版本管理
extra.apply {
set("compileSdkVersion", 34)
set("minSdkVersion", 24)
set("targetSdkVersion", 34)
// 依赖版本
set("compose_version", "1.5.0")
set("lifecycle_version", "2.6.1")
set("room_version", "2.5.2")
set("retrofit_version", "2.9.0")
set("tensorflow_version", "0.4.4")
}
// 模块级 build.gradle.kts (:app)
plugins {
id("com.android.application")
id("org.jetbrains.kotlin.android")
id("kotlin-kapt")
id("com.google.dagger.hilt.android")
id("com.google.devtools.ksp")
}
android {
namespace = "com.example.aiassistant"
compileSdk = 34
defaultConfig {
applicationId = "com.example.aiassistant"
minSdk = 24
targetSdk = 34
versionCode = 1
versionName = "1.0.0"
// 启用多dex
multiDexEnabled = true
// 构建配置
buildConfigField("String", "API_BASE_URL", "\"https://api.example.com\"")
buildConfigField("boolean", "ENABLE_DEBUG_FEATURES", "${BuildConfig.DEBUG}")
// 开启矢量图支持
vectorDrawables.useSupportLibrary = true
// 启用R8完整模式
proguardFiles(
getDefaultProguardFile("proguard-android-optimize.txt"),
"proguard-rules.pro"
)
}
buildTypes {
release {
isMinifyEnabled = true
isShrinkResources = true
isDebuggable = false
proguardFiles(
getDefaultProguardFile("proguard-android-optimize.txt"),
"proguard-rules.pro"
)
}
debug {
applicationIdSuffix = ".debug"
versionNameSuffix = "-DEBUG"
isDebuggable = true
}
create("staging") {
initWith(getByName("debug"))
applicationIdSuffix = ".staging"
versionNameSuffix = "-STAGING"
matchingFallbacks += listOf("debug", "release")
}
}
// 构建特性
buildFeatures {
compose = true
buildConfig = true
viewBinding = true
}
composeOptions {
kotlinCompilerExtensionVersion = "1.5.1"
}
compileOptions {
sourceCompatibility = JavaVersion.VERSION_17
targetCompatibility = JavaVersion.VERSION_17
isCoreLibraryDesugaringEnabled = true
}
kotlinOptions {
jvmTarget = "17"
freeCompilerArgs = freeCompilerArgs + listOf(
"-opt-in=kotlin.RequiresOptIn",
"-Xjvm-default=all",
"-Xstring-concat=indy-with-constants"
)
}
// 变体维度
flavorDimensions += listOf("environment", "aiEngine")
productFlavors {
create("development") {
dimension = "environment"
applicationIdSuffix = ".dev"
versionNameSuffix = "-dev"
}
create("production") {
dimension = "environment"
}
create("tflite") {
dimension = "aiEngine"
buildConfigField("String", "AI_ENGINE", "\"TFLITE\"")
}
create("cloud") {
dimension = "aiEngine"
buildConfigField("String", "AI_ENGINE", "\"CLOUD\"")
}
}
// 源集配置
sourceSets {
getByName("main") {
java.srcDirs("src/main/kotlin")
assets.srcDirs("src/main/assets")
res.srcDirs("src/main/res")
}
getByName("development") {
java.srcDirs("src/development/kotlin")
res.srcDirs("src/development/res")
}
getByName("androidTest") {
java.srcDirs("src/androidTest/kotlin")
assets.srcDirs("src/androidTest/assets")
}
}
// 测试选项
testOptions {
unitTests {
isIncludeAndroidResources = true
isReturnDefaultValues = true
}
animationsDisabled = true
}
// 打包选项
packaging {
resources {
excludes += "/META-INF/{AL2.0,LGPL2.1}"
excludes += "META-INF/*.kotlin_module"
excludes += "META-INF/*.version"
}
}
}
dependencies {
// 核心依赖
implementation(platform("androidx.compose:compose-bom:2023.08.00"))
// Jetpack Compose
implementation("androidx.compose.ui:ui")
implementation("androidx.compose.ui:ui-tooling-preview")
implementation("androidx.compose.material3:material3")
implementation("androidx.compose.material:material-icons-extended")
implementation("androidx.activity:activity-compose:1.7.2")
// AndroidX
implementation("androidx.core:core-ktx:1.12.0")
implementation("androidx.lifecycle:lifecycle-runtime-ktx:$lifecycle_version")
implementation("androidx.lifecycle:lifecycle-viewmodel-compose:$lifecycle_version")
implementation("androidx.navigation:navigation-compose:2.7.1")
// 协程
implementation("org.jetbrains.kotlinx:kotlinx-coroutines-android:1.7.3")
implementation("org.jetbrains.kotlinx:kotlinx-coroutines-core:1.7.3")
// 依赖注入
implementation("com.google.dagger:hilt-android:2.48")
kapt("com.google.dagger:hilt-compiler:2.48")
implementation("androidx.hilt:hilt-navigation-compose:1.0.0")
// 网络
implementation("com.squareup.retrofit2:retrofit:$retrofit_version")
implementation("com.squareup.retrofit2:converter-gson:$retrofit_version")
implementation("com.squareup.okhttp3:logging-interceptor:4.11.0")
// 数据库
implementation("androidx.room:room-runtime:$room_version")
implementation("androidx.room:room-ktx:$room_version")
ksp("androidx.room:room-compiler:$room_version")
// AI/ML
implementation("org.tensorflow:tensorflow-lite:$tensorflow_version")
implementation("org.tensorflow:tensorflow-lite-support:$tensorflow_version")
implementation("org.tensorflow:tensorflow-lite-gpu:$tensorflow_version")
implementation("org.tensorflow:tensorflow-lite-select-tf-ops:$tensorflow_version")
// 工具库
implementation("com.jakewharton.timber:timber:5.0.1")
implementation("com.airbnb.android:lottie-compose:6.1.0")
implementation("io.coil-kt:coil-compose:2.4.0")
// 反混淆
coreLibraryDesugaring("com.android.tools:desugar_jdk_libs:2.0.3")
// 测试
testImplementation("junit:junit:4.13.2")
testImplementation("org.jetbrains.kotlinx:kotlinx-coroutines-test:1.7.3")
testImplementation("io.mockk:mockk:1.13.5")
androidTestImplementation("androidx.test.ext:junit:1.1.5")
androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
androidTestImplementation("androidx.compose.ui:ui-test-junit4")
debugImplementation("androidx.compose.ui:ui-tooling")
debugImplementation("androidx.compose.ui:ui-test-manifest")
}5.2 模块化配置
// :build-logic 构建逻辑模块
// build-logic/convention/build.gradle.kts
plugins {
`kotlin-dsl`
}
dependencies {
implementation(gradleApi())
implementation(libs.android.gradle.plugin)
implementation(libs.kotlin.gradle.plugin)
implementation(libs.hilt.gradle.plugin)
}
// 自定义插件
abstract class AndroidFeatureConventionPlugin : Plugin<Project> {
override fun apply(target: Project) {
with(target) {
pluginManager.apply {
apply("com.android.library")
apply("org.jetbrains.kotlin.android")
apply("kotlin-kapt")
apply("com.google.dagger.hilt.android")
}
extensions.configure<BaseAppModuleExtension> {
defaultConfig {
minSdk = 24
}
buildFeatures {
compose = true
}
composeOptions {
kotlinCompilerExtensionVersion = "1.5.1"
}
dependencies {
add("implementation", project(":core:common"))
add("implementation", project(":core:ui"))
add("implementation", libs.hilt.android)
add("kapt", libs.hilt.compiler)
}
}
}
}
}
// 功能模块 (:feature-prediction) 的 build.gradle.kts
plugins {
id("android-feature-convention")
}
android {
namespace = "com.example.aiassistant.feature.prediction"
}
dependencies {
implementation(project(":core:network"))
implementation(project(":core:database"))
implementation(project(":ai:engine"))
implementation(libs.bundles.compose)
implementation(libs.bundles.lifecycle)
testImplementation(libs.bundles.unitTest)
androidTestImplementation(libs.bundles.androidTest)
}六、测试策略完整实现
代码作用：
确保代码质量，通过分层测试覆盖从单元到集成的全场景。
实现原理：
主要从业务逻辑和算法验证，模块间接口和交互验证。
技术创新点：
使用单元测试，集成测试，UI测试一起进行测试，声明式测试API来避免重复代码，利用Gradle并行执行加速测试。
// 6.1.1 领域层测试
class GeneratePredictionUseCaseTest {
private lateinit var useCase: GeneratePredictionUseCase
private lateinit var mockRepository: PredictionRepository
private lateinit var mockAIRouter: AIRouter
private lateinit var mockValidationService: ValidationService
@BeforeEach
fun setUp() {
mockRepository = mockk()
mockAIRouter = mockk()
mockValidationService = mockk()
useCase = GeneratePredictionUseCase(
mockRepository,
mockAIRouter,
mockValidationService
)
}
@Test
fun `should return cached prediction when similar exists`() = runTest {
// 给定
val input = PredictionInput(
text = "Test input",
type = PredictionType.TEXT
)
val cachedPrediction = Prediction(
id = "cached-id",
input = input,
result = PredictionResult(confidence = 0.9, label = "Test"),
createdAt = Instant.now()
)
coEvery { mockValidationService.validate(any()) } returns Result.success(input)
coEvery { mockRepository.findSimilar(any()) } returns cachedPrediction
// 当
val result = useCase(input)
// 则
assertTrue(result.isSuccess)
assertEquals(cachedPrediction, result.getOrNull())
coVerify(exactly = 0) { mockAIRouter.process(any()) }
}
@Test
fun `should return failure when validation fails`() = runTest {
// 给定
val input = PredictionInput(
text = "",
type = PredictionType.TEXT
)
val validationError = ValidationError("Input cannot be empty")
coEvery { mockValidationService.validate(any()) } returns
Result.failure(validationError)
// 当
val result = useCase(input)
// 则
assertTrue(result.isFailure)
assertEquals(validationError, result.exceptionOrNull())
}
}
// 6.1.2 UI测试
class PredictionScreenTest {
@get:Rule
val composeTestRule = createComposeRule()
@Test
fun shouldShowPredictionResult_whenPredictionSucceeds() {
// 给定
val fakeViewModel = FakePredictionViewModel().apply {
setUiState(PredictionUiState.Success(fakePrediction))
}
// 当
composeTestRule.setContent {
AppTheme {
PredictionScreen(viewModel = fakeViewModel)
}
}
// 则
composeTestRule
.onNodeWithText("Prediction Result")
.assertIsDisplayed()
composeTestRule
.onNodeWithText(fakePrediction.label, substring = true)
.assertIsDisplayed()
}
@Test
fun shouldShowError_whenPredictionFails() {
// 给定
val errorMessage = "Network error"
val fakeViewModel = FakePredictionViewModel().apply {
setUiState(PredictionUiState.Error(errorMessage))
}
// 当
composeTestRule.setContent {
AppTheme {
PredictionScreen(viewModel = fakeViewModel)
}
}
// 则
composeTestRule
.onNodeWithText("Error")
.assertIsDisplayed()
composeTestRule
.onNodeWithText(errorMessage)
.assertIsDisplayed()
}
}
// 6.1.3 集成测试
@HiltAndroidTest
@MediumTest
class PredictionFlowIntegrationTest {
@get:Rule
val hiltRule = HiltAndroidRule(this)
@get:Rule
val composeTestRule = createAndroidComposeRule<MainActivity>()
@Inject
lateinit var database: AppDatabase
@Inject
lateinit var apiService: AIService
@Before
fun setUp() {
hiltRule.inject()
// 准备测试数据
runBlocking {
database.clearAllTables()
}
}
@Test
fun completePredictionFlow_shouldSaveToDatabase() {
// 模拟网络响应
mockWebServer.enqueue(
MockResponse()
.setResponseCode(200)
.setBody(FakeData.predictionResponse)
)
// 执行UI操作
composeTestRule.onNodeWithText("New Prediction")
.performClick()
composeTestRule.onNodeWithTag("inputField")
.performTextInput("Test prediction input")
composeTestRule.onNodeWithText("Predict")
.performClick()
// 验证结果
composeTestRule.waitUntil(5000) {
composeTestRule
.onNodeWithText("Result:")
.fetchSemanticsNode().isVisible
}
// 验证数据库
runBlocking {
val predictions = database.predictionDao().getAll()
assertEquals(1, predictions.size)
}
}
}七、性能监控与优化
代码作用：
监控应用性能，优化用户体验，提前发现和解决性能问题。
实现原理：
启动过程分段监控，内存和网络一起监控，JankStats检测UI卡顿，最后进行崩溃监控，确保异常正常及时处理。
技术创新点：
基于内存压力动态调整缓存策略，后台预加载、资源预取确保资源顺利获取，建立性能基准进行性能监控，实时进行数据采集和分析。
7.1 应用性能监控
// 7.1.1 启动性能监控
class AppStartupMonitor @Inject constructor(
@ApplicationContext private val context: Context
) {
private val startupTime = mutableMapOf<String, Long>()
fun recordStartupPhase(phase: String) {
startupTime[phase] = System.currentTimeMillis()
}
fun logStartupMetrics() {
val totalTime = startupTime.values.last() - startupTime.values.first()
val metrics = startupTime.entries
.sortedBy { it.value }
.windowed(2)
.joinToString("\n") { (prev, current) ->
val duration = current.value - prev.value
"${current.key}: ${duration}ms"
}
Timber.d("""
====== App Startup Metrics ======
Total time: ${totalTime}ms
Phase breakdown:
$metrics
=================================
""".trimIndent())
// 上报到分析服务
if (totalTime > CRITICAL_STARTUP_THRESHOLD) {
reportCriticalStartup(totalTime)
}
}
}
// 7.1.2 内存监控
class MemoryMonitor @Inject constructor(
private val activityManager: ActivityManager
) {
data class MemoryStats(
val totalMemory: Long,
val usedMemory: Long,
val availableMemory: Long,
val isLowMemory: Boolean,
val memoryClass: Int
)
fun getMemoryStats(): MemoryStats {
val memoryInfo = ActivityManager.MemoryInfo()
activityManager.getMemoryInfo(memoryInfo)
val runtime = Runtime.getRuntime()
val usedMemory = runtime.totalMemory() - runtime.freeMemory()
val maxMemory = runtime.maxMemory()
return MemoryStats(
totalMemory = maxMemory,
usedMemory = usedMemory,
availableMemory = memoryInfo.availMem,
isLowMemory = memoryInfo.lowMemory,
memoryClass = activityManager.memoryClass
)
}
fun checkMemoryPressure(): MemoryPressure {
val stats = getMemoryStats()
val memoryUsagePercentage = (stats.usedMemory.toDouble() / stats.totalMemory) * 100
return when {
stats.isLowMemory -> MemoryPressure.CRITICAL
memoryUsagePercentage > 90 -> MemoryPressure.HIGH
memoryUsagePercentage > 70 -> MemoryPressure.MEDIUM
else -> MemoryPressure.LOW
}
}
fun optimizeForMemoryPressure(pressure: MemoryPressure) {
when (pressure) {
MemoryPressure.HIGH, MemoryPressure.CRITICAL -> {
// 清理缓存
ImageLoader.get(context).clearMemoryCache()
// 减少Bitmap质量
System.setProperty("android.bitmap_quality", "medium")
// 记录事件
Analytics.logEvent("memory_optimization_triggered", mapOf(
"pressure_level" to pressure.name
))
}
else -> {
// 正常模式
}
}
}
}八、总结
1.AI混合架构：
基于设备能力、网络状态、任务复杂度的智能路由算法，端侧AI与云端AI协同合作，运用隐私敏感的AI计算策略，在隐私和功能间取得平衡。
2.声明式架构融合：
将MVVM、Clean Architecture、MVI整合，将单向数据流与响应式编程，确保状态管理的可预测，可调试。
3.工程化实践创新：
完整的模块化开发、测试、部署流水线编程，从代码编写到性能监控，实现了多环境的灵活配置管理。
4.性能优化突破：
使用启动、内存、网络、渲染优化方案，通过智能优化决策系统进行性能监控，最后完成自动调优。
实际应用中，需要根据项目规模和团队情况进行适当裁剪。对于小型项目，可以简化架构；对于大型复杂项目，可能需要引入更多的设计模式和基础设施。
关键技术点总结：
Kotlin协程和Flow处理异步操作，Hilt实现依赖注入，Jetpack Compose构建现代化UI，TensorFlow Lite集成AI能力，模块化架构提高编译速度，分层测试确保代码质量，性能监控保证用户体验。
这个技术栈结合当下较为流行的技术，注重可维护性、可扩展性和开发效率的平衡。
作者：高海川
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