api-testing

name	api-testing
description	Comprehensive API testing including unit tests, integration tests, performance testing, and automated test pipelines. Use when testing REST endpoints, validating API responses, load testing, or setting up test automation.

API Testing Skill

技能概述

专业的API测试技能模块，专注于全面测试策略、性能测试和自动化测试流水线。

核心能力

• 功能测试: 单元测试、集成测试、端到端测试
• 性能测试: 负载测试、压力测试、并发测试
• 安全测试: 认证授权、输入验证、SQL注入防护
• API文档: 自动化文档生成、API契约测试
• 测试自动化: CI/CD集成、测试报告、质量门禁
• 监控测试: 生产环境监控、混沌工程

当前应用场景：足球预测API测试

• 测试框架: pytest + pytest-asyncio
• API测试: FastAPI TestClient + httpx
• 性能测试: locust + k6
• 覆盖率: pytest-cov
• 当前测试数: 279个测试函数

工具和库

• pytest: 测试框架
• httpx: 异步HTTP客户端
• locust: 负载测试
• pytest-cov: 覆盖率测试
• factory-boy: 测试数据工厂
• pytest-mock: Mock和Stub
• requests-mock: HTTP请求Mock

快速开始（第一层）

基础API测试

import pytest
from fastapi.testclient import TestClient
from main import app

client = TestClient(app)

def test_health_check():
    """健康检查测试"""
    response = client.get("/health")
    assert response.status_code == 200
    assert response.json()["status"] == "healthy"

def test_prediction_endpoint():
    """预测端点测试"""
    response = client.post(
        "/api/predict",
        json={
            "home_team": "Manchester United",
            "away_team": "Arsenal"
        }
    )
    assert response.status_code == 200
    data = response.json()
    assert "prediction" in data
    assert "confidence" in data

深入测试（第二层）

异步测试和集成测试

import pytest
import asyncio
from httpx import AsyncClient

@pytest.mark.asyncio
async def test_async_prediction():
    """异步预测测试"""
    async with AsyncClient(app=app, base_url="http://test") as client:
        response = await client.post(
            "/api/predict",
            json={"home_team": "Team A", "away_team": "Team B"}
        )
        assert response.status_code == 200
        data = response.json()
        assert data["prediction"] in ["HOME", "DRAW", "AWAY"]
        assert 0 <= data["confidence"] <= 1

@pytest.mark.asyncio
async def test_database_integration():
    """数据库集成测试"""
    # 设置测试数据库
    test_db = await setup_test_database()

    try:
        # 测试数据存储
        result = await save_prediction_to_db(test_prediction)
        assert result["success"] is True

        # 测试数据检索
        saved = await get_prediction_from_db(result["id"])
        assert saved["prediction"] == test_prediction["prediction"]

    finally:
        # 清理测试数据
        await cleanup_test_database(test_db)

高级应用（第三层）

性能测试

from locust import HttpUser, task, between

class FootballAPIUser(HttpUser):
    wait_time = between(1, 3)

    @task(3)
    def predict_match(self):
        """预测比赛任务"""
        self.client.post("/api/predict", json={
            "home_team": "Team A",
            "away_team": "Team B"
        })

    @task(1)
    def get_predictions(self):
        """获取预测历史任务"""
        self.client.get("/api/predictions")

    @task(1)
    def health_check(self):
        """健康检查任务"""
        self.client.get("/health")

# 运行性能测试
# locust -f performance_test.py --host=http://localhost:8000

测试策略

1. 测试金字塔

• 单元测试 (70%): 测试单个函数和类
• 集成测试 (20%): 测试组件间交互
• 端到端测试 (10%): 测试完整工作流

2. 测试分类

• 功能测试: 验证API功能正确性
• 性能测试: 验证API性能指标
• 安全测试: 验证API安全防护
• 兼容性测试: 验证API向后兼容性

覆盖率优化

目标覆盖率: 80%+

# 运行覆盖率测试
pytest --cov=src --cov-report=html --cov-report=term

# 生成覆盖率报告
coverage html
coverage report

覆盖率配置

# .coveragerc
[run]
source = src
omit =
    */tests/*
    */migrations/*
    */venv/*
    */__pycache__/*

[report]
exclude_lines =
    pragma: no cover
    def __repr__
    raise AssertionError
    raise NotImplementedError

自动化测试流水线

GitHub Actions配置

# .github/workflows/test.yml
name: API Tests

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: [3.11]

    services:
      postgres:
        image: postgres:15
        env:
          POSTGRES_PASSWORD: testpass
          POSTGRES_DB: testdb
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5

      redis:
        image: redis:7
        options: >-
          --health-cmd "redis-cli ping"
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5

    steps:
    - uses: actions/checkout@v3

    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: ${{ matrix.python-version }}

    - name: Install dependencies
      run: |
        pip install -r requirements.txt
        pip install -r requirements-dev.txt

    - name: Run linting
      run: |
        flake8 src tests
        black --check src tests
        mypy src

    - name: Run tests with coverage
      env:
        DATABASE_URL: postgresql://postgres:testpass@localhost/testdb
        REDIS_URL: redis://localhost:6379
      run: |
        pytest --cov=src --cov-report=xml --cov-report=html

    - name: Upload coverage to Codecov
      uses: codecov/codecov-action@v3

    - name: Run performance tests
      run: |
        locust -f tests/performance/locustfile.py \
          --headless \
          --users 10 \
          --spawn-rate 2 \
          --run-time 30s \
          --host http://localhost:8000

最佳实践

1. 测试命名: 使用描述性的测试名称
2. 测试隔离: 确保测试之间相互独立
3. 测试数据: 使用工厂模式生成测试数据
4. Mock使用: 合理使用Mock避免外部依赖
5. 断言清晰: 使用清晰的断言信息
6. 测试维护: 定期更新和重构测试

Related Skills

• fastapi-development: FastAPI async API development
• code-quality: Code quality management
• performance-monitoring: System performance monitoring
• football-prediction: Football prediction system

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Last updated: 2025-12-28 Target: API测试覆盖率和质量提升