武大胆

App Technical Architecture#

A typical App technical architecture is divided into Client and Server parts, achieving data interaction through network communication. Below is a layered architecture diagram:

Client Technical Architecture#

Client Layering#

• UI Layer (View)

  • Responsible for interface rendering and user interaction.
  • Technical Implementation:
    • Android: XML layout + Jetpack Compose (declarative UI).
    • iOS: Storyboard/SwiftUI.
    • Cross-platform: Flutter (Dart), React Native (JavaScript).

• Business Logic Layer (ViewModel/Presenter)

  • Handles business logic, data transformation, and state management.
  • Technical Implementation:
    • Android: ViewModel + LiveData.
    • iOS: Combine framework (Swift).
    • Cross-platform: Bloc pattern (Flutter), Redux (React Native).

• Network Layer (Repository/DataSource)

  • Encapsulates API requests, caching strategies, and error handling.
  • Technical Implementation:
    • Android: Retrofit + OkHttp.
    • iOS: Alamofire (Swift) or URLSession.
    • Cross-platform: Dio (Flutter), Axios (React Native).

• Local Storage Layer (Local Storage)

  • Manages local data persistence.
  • Technical Implementation:
    • SQLite: Room (Android), Core Data (iOS).
    • Key-value storage: SharedPreferences (Android), UserDefaults (iOS).
    • File storage: File System API.

Client Technology Stack#

Server Technical Architecture#

Server Layering#

• API Gateway Layer (API Gateway)

  • Unified entry point, handling routing, authentication, rate limiting, and logging.
  • Technical Implementation: Nginx, Kong, Spring Cloud Gateway.

• Business Logic Layer (Application Layer)

  • Implements core business logic (e.g., order processing, user management).
  • Technical Implementation:
    • Java: Spring Boot, Micronaut.
    • Python: Django, FastAPI.
    • Node.js: Express, NestJS.
    • Go: Gin, Echo.

• Data Storage Layer (Data Storage)

  • Persistent data storage and querying.
  • Technical Implementation:
    • Relational databases: MySQL, PostgreSQL.
    • Non-relational databases: MongoDB, Redis (cache).
    • Big data storage: HBase, Elasticsearch (search).

• Message Queues and Asynchronous Processing

  • Decouples high-concurrency tasks (e.g., email sending, asynchronous order processing).
  • Technical Implementation: Kafka, RabbitMQ, Celery (Python).

• Microservices and Containerization (Optional)

  • Breaks complex systems into independent services to enhance scalability.
  • Technical Implementation: Docker, Kubernetes, gRPC.

Server Technology Stack#

Client and Server Interaction Process#

Typical Interaction Process (Taking User Login as an Example)#

1. Client Initiates Request

   • User inputs account password, and the client sends a login request via HTTPS (POST /api/login).
   • Data format: JSON (e.g., {"username": "user1", "password": "***"}).

2. Server Processes Request

   • API Gateway verifies the request's legality (e.g., IP whitelist, rate limiting).
   • Business logic layer verifies account password and generates Token (JWT).
   • Database queries user information (MySQL SELECT * FROM users WHERE username = ?).

3. Server Returns Response

   • Success: returns 200 OK + Token and user data.
   • Failure: returns 401 Unauthorized + error code.

4. Client Processes Response

   • Parses response data, stores Token locally (SharedPreferences/Keychain).
   • Redirects to the homepage, updates UI state.

Communication Protocol and Data Format#

• Protocol: HTTP/HTTPS (RESTful API), WebSocket (real-time communication).
• Data Format: JSON (mainstream), Protocol Buffers (high-performance scenarios).
• Security Mechanism: JWT authentication, OAuth 2.0 (third-party login), SSL/TLS encryption.

App Testing#

Testing Process#

1. Requirement Analysis Phase

   • Clarify testing objectives: understand functional requirements, user scenarios, and non-functional requirements (e.g., performance, security).
   • Write testing requirement documents: determine testing scope, priority, and acceptance criteria.

2. Testing Planning Phase

   • Develop testing strategy: determine testing types (functional, performance, compatibility, etc.), resource allocation, and schedule.
   • Risk assessment: identify potential risks (e.g., device fragmentation, network environment differences) and develop response plans.

3. Test Case Design

   • Write test cases based on requirements, covering positive, negative scenarios, and boundary conditions.
   • Use design methods such as equivalence class partitioning, boundary value analysis, and scenario method.

4. Test Environment Setup

   • Prepare hardware devices (real devices, emulators), network environment, test data, and toolchain.

5. Test Execution

   • Execute test cases by priority, recording test results and defects.
   • Conduct in phases: smoke testing → functional testing → regression testing → acceptance testing.

6. Defect Management

   • Use tools (e.g., JIRA, Bugzilla) to track the defect lifecycle (submission → fixing → verification → closure).
   • Analyze defect distribution and optimize testing focus.

7. Test Report and Summary

   • Output test report: coverage, defect statistics, risk analysis, and improvement suggestions.
   • Review the testing process and optimize subsequent testing strategies.

Testing Methods#

1. Black Box Testing

   • Focuses on input and output, not involving internal code logic.
   • Typical methods: functional testing, user experience testing, installation and uninstallation testing.

2. White Box Testing

   • Designs test cases based on code logic, covering branches, paths, and conditions.
   • Common techniques: code coverage analysis (e.g., JaCoCo), static code analysis (e.g., SonarQube).

3. Gray Box Testing

   • Combines black box and white box testing, locating issues through interface or log analysis.

4. Special Testing Types

   • Functional Testing: Verifies core functionalities (e.g., login, payment) meet requirements.
   • Performance Testing: Response time, memory/CPU usage, startup speed (tools: GT, PerfDog).
   • Compatibility Testing: Covers different devices, OS versions, screen resolutions, network environments (tools: Firebase Test Lab).
   • Security Testing: Data encryption, permission management, vulnerability scanning (tools: OWASP ZAP, Burp Suite).
   • User Experience Testing: Interface usability, interaction smoothness, multilingual support.
   • Installation and Uninstallation Testing: Installation package size, upgrade coverage, residual file cleanup.
   • Interface Testing: Verifies API calls and data transmission (tools: Postman, Charles).
   • Regression Testing: Automated scripts cover main processes to ensure no regression issues in new versions.

Testing Technologies#

1. Automated Testing

   • UI Automation: Appium (cross-platform), Espresso (Android), XCUITest (iOS).
   • Interface Automation: RestAssured, Python Requests.
   • Performance Automation: JMeter, LoadRunner.

2. Cloud Testing Platforms

   • Use platforms like AWS Device Farm, Sauce Labs for parallel testing across multiple devices.

3. Monkey Testing

   • Detect crashes and ANR through random events (clicks, swipes) (tools: Android ADB Monkey).

4. A/B Testing

   • Compare user behavior data across different versions to optimize feature design (tools: Firebase A/B Testing).

5. Code Static Analysis

   • Use Lint tools to detect code standards, memory leaks, and potential crashes.

6. Network Simulation Tools

   • Simulate weak networks, delays, packet loss (tools: Network Link Conditioner, Charles Proxy).

Testing Environment#

1. Hardware Environment

   • Real devices covering mainstream brands (e.g., iPhone, Huawei, Samsung) and different configurations (CPU, memory).
   • Emulators/virtual machines: Android Studio Emulator, iOS Simulator.

2. Software Environment

   • Operating Systems: Android (different versions and custom ROMs), iOS (latest and old versions).
   • Third-party dependencies: SDK versions (e.g., Google Play Services), database compatibility.

3. Network Environment

   • Test different network types (Wi-Fi, 4G/5G), weak networks (2G/high latency), and offline scenarios.

4. Data Environment

   • Prepare test data: normal data, abnormal data (e.g., overly long text, special characters).
   • Data isolation: avoid test data contaminating the production environment.

5. Continuous Integration (CI) Environment

   • Integrate tools like Jenkins, GitLab CI to achieve automated build → test → report processes.

6. Security Testing Environment

   • Set up a sandbox environment to simulate man-in-the-middle attacks, data tampering, and other security threats.